Usage category dashboard

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Summary

Usage category Rows (entries) Model shared (count) Model shared (%) Evaluated (count) Evaluated (%) Avg dev sample size Avg eval sample size
Information extraction 753 20 2.7 741 98.4 28999.9 1212.3
Deidentification 33 6 18.2 33 100.0 2352.0 1041.8
Language normalization, representation, and modeling 16 11 68.8 15 93.8 9324.2 2641.2
Generative applications 5 0 0.0 3 60.0 18.8 367.7

Models

Model abbreviation NLP task description Usage category Model shared Evaluated Author Year Article title Code link
DEDUCE Deidentification Yes Yes Mosteiro 2025 Investigating De-Identification Methodologies in Dutch Medical Texts: A Replication Study of Deduce and Deidentify link
DEDUCE Removal of Protected Health Information Deidentification Yes Yes Menger 2018 DEDUCE: A pattern matching method for automatic de- identification of Dutch medical text link
Scheurwegs-OMSVM Deidentification No Yes Scheurwegs 2013 De-Identification of Clinical Free Text in Dutch with Limited Training Data: A Case Study
Scheurwegs-OOSVM Deidentification No Yes Scheurwegs 2013 De-Identification of Clinical Free Text in Dutch with Limited Training Data: A Case Study
Scheurwegs-RF10 Deidentification No Yes Scheurwegs 2013 De-Identification of Clinical Free Text in Dutch with Limited Training Data: A Case Study
T25-DeepSeek-R1-14B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-Domain-BERT-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Domain-Longformer-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Domain-Longformer-large Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Domain-RoBERTa-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Domain-RoBERTa-large Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Gemma-2-2B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-Gemma-2-9B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-General-BERT-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-General-Longformer-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-General-Longformer-large Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-General-RoBERTa-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-General-RoBERTa-large Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Llama-3.1-8B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-Llama-3.2-3B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-Llama-3.3-70B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-Mistral-Nemo-12B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-Mixed-BERT-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Mixed-Longformer-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Mixed-Longformer-large Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Mixed-RoBERTa-base Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Mixed-RoBERTa-large Task 25: Anonymization, sequence tagging Deidentification No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T25-Phi-4-14B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T25-Qwen-2.5-14B Task 25: Anonymization, sequence tagging Deidentification No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
Trienes-BiLSTM-CRF Deidentification Yes Yes Mosteiro 2025 Investigating De-Identification Methodologies in Dutch Medical Texts: A Replication Study of Deduce and Deidentify
Trienes-BiLSTM-CRF Deidentification Yes Yes Trienes 2020 Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
Trienes-CRF Deidentification Yes Yes Trienes 2020 Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
Trienes-DEDUCE Deidentification Yes Yes Trienes 2020 Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
Autoscriber Clinical conversation summarization (digital scribe) Generative applications No Yes van Buchem 2024 Impact of a Digital Scribe System on Clinical Documentation Time and Quality: Usability Study link
GPT-4 Medical text summarization Generative applications No No Schoonbeek 2025 Completeness, correctness and conciseness of physician-written versus large language model generated patient summaries integrated in electronic health records
GPT-4 Automatic generation of draft replies to patient messages Generative applications No Yes Bootsma-Robroeks 2025 AI-generated draft replies to patient messages: exploring effects of implementation
GPT-4-Turbo version 1106 To generate discharge letters from EHR data Generative applications No No de Hond 2025 Hallucinations, Omissions, and Usability: A Comparison between GPT-Generated and Physician Discharge Letters for Clinical Use link
Hybrid (EDA + BI-RADS) To automatically summarize Dutch radiology reports and assign a BI-RADS score Generative applications No Yes Nguyen 2020 A Hybrid Text Classification and Language Generation Model for Automated Summarization of Dutch Breast Cancer Radiology Reports link
Arends-ALLRule extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes No Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Arends-BOW extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes Yes Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Arends-CNN extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes Yes Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Arends-MedCAT extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes Yes Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Arends-MedRoBERTa extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes No Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Arends-SetFit-RobBERT extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes Yes Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Arends-SpanCategorizer extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes Yes Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Arends-biGRU extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes Yes Arends 2024 Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification link
Bagheri-AVG-W2V ICD-10 Information extraction No Yes Bagheri 2020 Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters link
Bagheri-BOW-SVM ICD-10 Information extraction No Yes Bagheri 2020 Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters link
Bagheri-BiGRU ICD-10, specifically the four-character ICD-10 codes that are clinically\nrelevant such as atrial fibrillation (I48), acute myocardial infarction (I21) or dilated cardiomyopathy (I42.0). Information extraction No Yes Bagheri 2021 Automatic multilabel detection of ICD10 codes in Dutch cardiology discharge letters using neural networks link
Bagheri-BiLSTM ICD-10 Information extraction No Yes Bagheri 2020 Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters link
Bagheri-CNN1 ICD-10 Information extraction No Yes Bagheri 2020 Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters link
Bagheri-CNN2 ICD-10 Information extraction No Yes Bagheri 2020 Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters link
Bagheri-HA-GRU ICD-10 Information extraction No Yes Bagheri 2020 Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters link
Bagheri-LSTM ICD-10 Information extraction No Yes Bagheri 2020 Automatic ICD-10 Classification of Diseases from Dutch Discharge Letters link
Burgt-TM extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction Yes Yes Burgt 2024 Development of a text mining algorithm for identifying adverse drug reactions in electronic health records
CDC-CTCue-queries We selected two confirmed safety signals and two suspected drug-event associations (hereafter referred to as potential safety signals) generated by the SRS of the Nether- lands Pharmacovigilance Centre Lareb. Targeted searches were performed in the structured and unstructured fields of the EHR to identify additional cases for these signals using a text-mining software CTcue. Information extraction partially Yes Kalkhoran 2024 An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records
CDC-CTCue-queries generally presented in RCTs evaluating new drug therapies in mRCC were collected: Extraction of patient-related characteristics, treatment-related variables, comorbidities, adverse drug events, IMDC-criteria Information extraction partially Yes Laar 2020 An Electronic Health Record Text Mining Tool to Collect Real-World Drug Treatment Outcomes: A Validation Study in Patients With Metastatic Renal Cell Carcinoma
Cara-Llama-3-classify WHO performance status classification Information extraction Yes Yes Cara 2025 Automating Performance Status Annotation in Oncology Using Llama-3 link
Cara-Llama-3-score WHO performance score regression Information extraction Yes Yes Cara 2025 Automating Performance Status Annotation in Oncology Using Llama-3 link
ContextD In this paper we focus on the binary context-property negation. The label negated was given when evidence in the text was found that indicated that a specific event or condition did not take place or exist, otherwise the label not negated was assigned. Information extraction - Yes Es 2023 Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods link
ContextD each of the recognized terms in the corpus was annotated for the three contextual properties: negation, temporality, and experiencer. Information extraction partially Yes Afzal 2014 ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus
Es-BiLSTM In this paper we focus on the binary context-property negation. The label negated was given when evidence in the text was found that indicated that a specific event or condition did not take place or exist, otherwise the label not negated was assigned. Information extraction No Yes Es 2023 Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods link
Es-RoBERTa In this paper we focus on the binary context-property negation. The label negated was given when evidence in the text was found that indicated that a specific event or condition did not take place or exist, otherwise the label not negated was assigned. Information extraction Yes No Es 2023 Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods link
Es-Voting ensemble In this paper we focus on the binary context-property negation. The label negated was given when evidence in the text was found that indicated that a specific event or condition did not take place or exist, otherwise the label not negated was assigned. Information extraction No Yes Es 2023 Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods link
GPT-3.5 To assign BI-RADS categories using only the findings described by the radiologists Information extraction No Yes Cozzi 2024 BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study link
GPT-4 To assign BI-RADS categories using only the findings described by the radiologists Information extraction No Yes Cozzi 2024 BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study link
Gemini To assign BI-RADS categories using only the findings described by the radiologists Information extraction No Yes Cozzi 2024 BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study link
HAGALBERT lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction No Yes Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models link
Hendrix-regex to identify pulmonary nodules described in radiol - ogy reports Information extraction Yes No Hendrix 2023 Trends in the incidence of pulmonary nodules in chest computed tomography: 10‑year results from two Dutch hospitals
Homburg-BERT Detecting covid-19 consultations Information extraction No Yes Homburg 2023 A Natural Language Processing Model for COVID-19 Detection Based on Dutch General Practice Electronic Health Records by Using Bidirectional Encoder Representations From Transformers: Development and Validation Study
Homburg-BERT Information extraction No No Veldman 2025 Hasselt Corona Impact Study: Impact of COVID-19 on healthcare seeking in a small Dutch town
Kim-MedRoberta-FT we present an automated coding of natural language descriptions of functioning in EHRs into standardized categories according to ICF (Classification of Functioning, Disability and Health), specifically 9 codes relevant for COVID-19 (Tab 1) Information extraction Yes No Kim 2022 Modeling Dutch Medical Texts for Detecting Functional Categories and Levels of COVID-19 Patients link
Klappe-RB Contextual property detection: Uncertainty, Laterality, Temporality extraction Information extraction Yes No Klappe 2021 Contextual property detection in Dutch diagnosis descriptions for uncertainty, laterality and temporality link
Krastman-HW To identify patients with diagnoses of a hand or wrist disorder Information extraction Yes Yes Krastman 2024 Incidence of hand and wrist disorders in primary care: a retrospective cohort study.
MedRoBERTa.nl-HAGA lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction No Yes Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models link
Muizelaar-NB lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction No No Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models
Muizelaar-RF lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction No No Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models
Muizelaar-SGD lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction No Yes Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models link
Muizelaar-StrMatch lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction Yes Yes Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models link
Nobel-RB quantify T-stage of pulmonary tumors according to the tumor node metastasis (TNM) classification Information extraction Yes No Nobel 2021 T-staging pulmonary oncology from radiological reports using natural language processing: translating into a multi-language setting link
Olthof-ANN-300bi200tri classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-ANN-500bi classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-ANN-500uni classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-BERT classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-NB-300bi200tri classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-NB-500bi classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-NB-500uni classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-RB-raw classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction Yes No Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-RF-300bi200tri classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-RF-500bi classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-RF-500uni classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-RF-all classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-RF-all-fsel classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Olthof-W2V classify radiology reports in orthopaedic trauma for the presence of injuries. ; Labeling (presence or absence of pneumothorax and lat- erality based on the information in the radiology report) was per- formed Information extraction No Yes Olthof 2021 Machine learning based natural language processing of radiology reports in orthopaedic trauma
Rietberg-BERTje-End2End Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-BERTje-LR Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-BERTje-RF Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-BERTje-SVM Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-Doc2vec-LR Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-Doc2vec-RF Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-Doc2vec-SVM Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-MedRoBERTa.nl-End2End Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-MedRoBERTa.nl-LR Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-MedRoBERTa.nl-RF Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-MedRoBERTa.nl-SVM Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-RobBERT-End2End Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-RobBERT-LR Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-RobBERT-RF Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-RobBERT-SVM Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-TFIDF-LR Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-TFIDF-RF Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
Rietberg-TFIDF-SVM Extracting the reason for taking an MRI scan of Multiple Sclerosis (MS) patients using the attached free-form reports: Diagnosis, Progression or Monitoring Information extraction No Yes Rietberg 2023 Accurate and Reliable Classification of Unstructured Reports on Their Diagnostic Goal Using BERT Models link
RobBERT-HAGA lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction No Yes Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models link
Scheurwegs-ADV ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-AvW2V-LMI ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-BOC ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-BPM ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-CW2V-LMI ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-Dict ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-LMI ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-MajVote ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-NB-BN-BoW Information extraction No Yes Scheurwegs 2015 Data integration of structured and unstructured sources for assigning clinical codes to patient stays
Scheurwegs-NB-BN-Early-integration Information extraction No Yes Scheurwegs 2015 Data integration of structured and unstructured sources for assigning clinical codes to patient stays
Scheurwegs-NB-BN-Late-integration Information extraction No Yes Scheurwegs 2015 Data integration of structured and unstructured sources for assigning clinical codes to patient stays
Scheurwegs-NW2V-LMI ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-NW2V-UMLS-LMI ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Scheurwegs-UMLSN ICD-9-CM coding Information extraction No Yes Scheurwegs 2017 Assigning clinical codes with data-driven concept representation on Dutch clinical free text
Schuemie-C4.5 Classifying GP records as either a liver disorder case or not Information extraction No Yes Schuemie 2012 Automating classification of free-text electronic health records for epidemiological studies
Schuemie-KNN Classifying GP records as either a liver disorder case or not Information extraction No Yes Schuemie 2012 Automating classification of free-text electronic health records for epidemiological studies
Schuemie-MyC Classifying GP records as either a liver disorder case or not Information extraction No Yes Schuemie 2012 Automating classification of free-text electronic health records for epidemiological studies
Schuemie-NB Classifying GP records as either a liver disorder case or not Information extraction No Yes Schuemie 2012 Automating classification of free-text electronic health records for epidemiological studies
Schuemie-RF Classifying GP records as either a liver disorder case or not Information extraction No Yes Schuemie 2012 Automating classification of free-text electronic health records for epidemiological studies
Schuemie-RIPPER Classifying GP records as either a liver disorder case or not Information extraction No Yes Schuemie 2012 Automating classification of free-text electronic health records for epidemiological studies
Schuemie-SVM Classifying GP records as either a liver disorder case or not Information extraction No Yes Schuemie 2012 Automating classification of free-text electronic health records for epidemiological studies
Seinen-AVGEMB-LR Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-AVGEMB-LR Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-AVGEMB-NN Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-AVGEMB-NN Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-AVGEMB-XGB Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-AVGEMB-XGB Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-TFIDF-LR Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-TFIDF-LR Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-TFIDF-NN Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-TFIDF-NN Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-TFIDF-XGB Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-TFIDF-XGB Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Seinen-search-terms Classifying free text consults with ICPC codes Information extraction No Yes Seinen 2024 Using clinical text to refine unspecific condition codes in Dutch general practitioner EHR data
Siegersma-RB-W2B-V1 extraction of medication and Adverse Drug Reaction Identification in Clinical Notes Information extraction No Yes Siegersma 2022 Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching
Siegersma-RB-W2B-V2 extraction of medication and Adverse Drug Reaction Identification in Clinical Notes Information extraction No Yes Siegersma 2022 Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching
Siegersma-RB-W2B-V3 extraction of medication and Adverse Drug Reaction Identification in Clinical Notes Information extraction No Yes Siegersma 2022 Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching
Siegersma-RB-W2B-V4 extraction of medication and Adverse Drug Reaction Identification in Clinical Notes Information extraction No Yes Siegersma 2022 Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching
Siegersma-RB-W2B-V5 extraction of medication and Adverse Drug Reaction Identification in Clinical Notes Information extraction No Yes Siegersma 2022 Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching
Siegersma-RB-W2B-V6 extraction of medication and Adverse Drug Reaction Identification in Clinical Notes Information extraction No Yes Siegersma 2022 Development of a Pipeline for Adverse Drug Reaction Identification in Clinical Notes: Word Embedding Models and String Matching
Spyns-RB Using many many many idfferent subsystems, parsers etc Information extraction No Yes Spyns 1996 A Dutch medical language processor
Straalen-DD A text-mining algorithm to determine the diagnosis "Dupuytren’s contracture" from the free text fields Information extraction partially Yes Straalen 2024 The incidence and prevalence of Dupuytren’s disease in primary care: results from a text mining approach on registration data
T1-DeepSeek-R1-14B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-Domain-BERT-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Domain-Longformer-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Domain-Longformer-large Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Domain-RoBERTa-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Domain-RoBERTa-large Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Gemma-2-2B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-Gemma-2-9B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-General-BERT-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-General-Longformer-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-General-Longformer-large Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-General-RoBERTa-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-General-RoBERTa-large Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Llama-3.1-8B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-Llama-3.2-3B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-Llama-3.3-70B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-Mistral-Nemo-12B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-Mixed-BERT-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Mixed-Longformer-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Mixed-Longformer-large Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Mixed-RoBERTa-base Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Mixed-RoBERTa-large Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T1-Phi-4-14B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T1-Qwen-2.5-14B Task 1: Adhesion presence. Predict adhesion presence (binary) based on the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-DeepSeek-R1-14B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-Domain-BERT-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Domain-Longformer-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Domain-Longformer-large Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Domain-RoBERTa-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Domain-RoBERTa-large Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Gemma-2-2B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-Gemma-2-9B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-General-BERT-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-General-Longformer-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-General-Longformer-large Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-General-RoBERTa-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-General-RoBERTa-large Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Llama-3.1-8B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-Llama-3.2-3B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-Llama-3.3-70B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-Mistral-Nemo-12B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-Mixed-BERT-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Mixed-Longformer-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Mixed-Longformer-large Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Mixed-RoBERTa-base Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Mixed-RoBERTa-large Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T10-Phi-4-14B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T10-Qwen-2.5-14B Task 10: Prostate radiology suspicious lesions on prostate MRI, predict the number of PI-RADS 3 – 5 lesions based on the radiology report.\npredic2on as a string, being “0”, “1”, “2”, “3”, or “4”. Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-DeepSeek-R1-14B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-Domain-BERT-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Domain-Longformer-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Domain-Longformer-large Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Domain-RoBERTa-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Domain-RoBERTa-large Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Gemma-2-2B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-Gemma-2-9B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-General-BERT-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-General-Longformer-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-General-Longformer-large Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-General-RoBERTa-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-General-RoBERTa-large Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Llama-3.1-8B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-Llama-3.2-3B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-Llama-3.3-70B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-Mistral-Nemo-12B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-Mixed-BERT-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Mixed-Longformer-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Mixed-Longformer-large Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Mixed-RoBERTa-base Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Mixed-RoBERTa-large Task 11: Prostate histopathology significant cancers Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T11-Phi-4-14B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T11-Qwen-2.5-14B Task 11: Prostate histopathology significant cancers Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-DeepSeek-R1-14B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-Domain-BERT-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Domain-Longformer-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Domain-Longformer-large Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Domain-RoBERTa-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Domain-RoBERTa-large Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Gemma-2-2B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-Gemma-2-9B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-General-BERT-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-General-Longformer-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-General-Longformer-large Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-General-RoBERTa-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-General-RoBERTa-large Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Llama-3.1-8B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-Llama-3.2-3B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-Llama-3.3-70B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-Mistral-Nemo-12B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-Mixed-BERT-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Mixed-Longformer-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Mixed-Longformer-large Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Mixed-RoBERTa-base Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Mixed-RoBERTa-large Task 12: Histopathology tissue type Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T12-Phi-4-14B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T12-Qwen-2.5-14B Task 12: Histopathology tissue type Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-DeepSeek-R1-14B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-Domain-BERT-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Domain-Longformer-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Domain-Longformer-large Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Domain-RoBERTa-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Domain-RoBERTa-large Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Gemma-2-2B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-Gemma-2-9B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-General-BERT-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-General-Longformer-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-General-Longformer-large Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-General-RoBERTa-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-General-RoBERTa-large Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Llama-3.1-8B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-Llama-3.2-3B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-Llama-3.3-70B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-Mistral-Nemo-12B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-Mixed-BERT-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Mixed-Longformer-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Mixed-Longformer-large Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Mixed-RoBERTa-base Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Mixed-RoBERTa-large Task 13: Histopathology tissue origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T13-Phi-4-14B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T13-Qwen-2.5-14B Task 13: Histopathology tissue origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-DeepSeek-R1-14B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-Domain-BERT-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Domain-Longformer-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Domain-Longformer-large Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Domain-RoBERTa-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Domain-RoBERTa-large Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Gemma-2-2B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-Gemma-2-9B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-General-BERT-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-General-Longformer-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-General-Longformer-large Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-General-RoBERTa-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-General-RoBERTa-large Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Llama-3.1-8B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-Llama-3.2-3B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-Llama-3.3-70B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-Mistral-Nemo-12B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-Mixed-BERT-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Mixed-Longformer-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Mixed-Longformer-large Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Mixed-RoBERTa-base Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Mixed-RoBERTa-large Task 15: Colon histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T15-Phi-4-14B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T15-Qwen-2.5-14B Task 15: Colon histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-DeepSeek-R1-14B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-Domain-BERT-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Domain-Longformer-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Domain-Longformer-large Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Domain-RoBERTa-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Domain-RoBERTa-large Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Gemma-2-2B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-Gemma-2-9B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-General-BERT-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-General-Longformer-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-General-Longformer-large Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-General-RoBERTa-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-General-RoBERTa-large Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Llama-3.1-8B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-Llama-3.2-3B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-Llama-3.3-70B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-Mistral-Nemo-12B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-Mixed-BERT-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Mixed-Longformer-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Mixed-Longformer-large Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Mixed-RoBERTa-base Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Mixed-RoBERTa-large Task 16: RECIST lesion size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T16-Phi-4-14B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T16-Qwen-2.5-14B Task 16: RECIST lesion size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-DeepSeek-R1-14B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-Domain-BERT-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Domain-Longformer-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Domain-Longformer-large Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Domain-RoBERTa-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Domain-RoBERTa-large Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Gemma-2-2B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-Gemma-2-9B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-General-BERT-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-General-Longformer-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-General-Longformer-large Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-General-RoBERTa-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-General-RoBERTa-large Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Llama-3.1-8B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-Llama-3.2-3B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-Llama-3.3-70B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-Mistral-Nemo-12B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-Mixed-BERT-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Mixed-Longformer-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Mixed-Longformer-large Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Mixed-RoBERTa-base Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Mixed-RoBERTa-large Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T17-Phi-4-14B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T17-Qwen-2.5-14B Task 17: Pancreatic ductal adenocarcinoma attributes Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-DeepSeek-R1-14B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-Domain-BERT-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Domain-Longformer-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Domain-Longformer-large Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Domain-RoBERTa-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Domain-RoBERTa-large Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Gemma-2-2B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-Gemma-2-9B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-General-BERT-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-General-Longformer-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-General-Longformer-large Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-General-RoBERTa-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-General-RoBERTa-large Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Llama-3.1-8B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-Llama-3.2-3B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-Llama-3.3-70B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-Mistral-Nemo-12B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-Mixed-BERT-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Mixed-Longformer-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Mixed-Longformer-large Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Mixed-RoBERTa-base Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Mixed-RoBERTa-large Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T18-Phi-4-14B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T18-Qwen-2.5-14B Task 18: Hip Kellgren-Lawrence scoring Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-DeepSeek-R1-14B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-Domain-BERT-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Domain-Longformer-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Domain-Longformer-large Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Domain-RoBERTa-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Domain-RoBERTa-large Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Gemma-2-2B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-Gemma-2-9B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-General-BERT-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-General-Longformer-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-General-Longformer-large Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-General-RoBERTa-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-General-RoBERTa-large Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Llama-3.1-8B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-Llama-3.2-3B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-Llama-3.3-70B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-Mistral-Nemo-12B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-Mixed-BERT-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Mixed-Longformer-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Mixed-Longformer-large Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Mixed-RoBERTa-base Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Mixed-RoBERTa-large Task 19: Prostate volume extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T19-Phi-4-14B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T19-Qwen-2.5-14B Task 19: Prostate volume extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-DeepSeek-R1-14B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-Domain-BERT-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Domain-Longformer-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Domain-Longformer-large Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Domain-RoBERTa-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Domain-RoBERTa-large Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Gemma-2-2B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-Gemma-2-9B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-General-BERT-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-General-Longformer-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-General-Longformer-large Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-General-RoBERTa-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-General-RoBERTa-large Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Llama-3.1-8B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-Llama-3.2-3B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-Llama-3.3-70B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-Mistral-Nemo-12B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-Mixed-BERT-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Mixed-Longformer-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Mixed-Longformer-large Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Mixed-RoBERTa-base Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Mixed-RoBERTa-large Task 2: Pulmonary nodule presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T2-Phi-4-14B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T2-Qwen-2.5-14B Task 2: Pulmonary nodule presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-DeepSeek-R1-14B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-Domain-BERT-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Domain-Longformer-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Domain-Longformer-large Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Domain-RoBERTa-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Domain-RoBERTa-large Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Gemma-2-2B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-Gemma-2-9B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-General-BERT-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-General-Longformer-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-General-Longformer-large Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-General-RoBERTa-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-General-RoBERTa-large Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Llama-3.1-8B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-Llama-3.2-3B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-Llama-3.3-70B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-Mistral-Nemo-12B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-Mixed-BERT-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Mixed-Longformer-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Mixed-Longformer-large Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Mixed-RoBERTa-base Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Mixed-RoBERTa-large Task 20: Prostate specific antigen extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T20-Phi-4-14B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T20-Qwen-2.5-14B Task 20: Prostate specific antigen extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-DeepSeek-R1-14B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-Domain-BERT-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Domain-Longformer-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Domain-Longformer-large Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Domain-RoBERTa-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Domain-RoBERTa-large Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Gemma-2-2B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-Gemma-2-9B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-General-BERT-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-General-Longformer-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-General-Longformer-large Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-General-RoBERTa-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-General-RoBERTa-large Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Llama-3.1-8B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-Llama-3.2-3B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-Llama-3.3-70B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-Mistral-Nemo-12B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-Mixed-BERT-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Mixed-Longformer-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Mixed-Longformer-large Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Mixed-RoBERTa-base Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Mixed-RoBERTa-large Task 21: Prostate specific antigen density extraction Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T21-Phi-4-14B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T21-Qwen-2.5-14B Task 21: Prostate specific antigen density extraction Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-DeepSeek-R1-14B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-Domain-BERT-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Domain-Longformer-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Domain-Longformer-large Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Domain-RoBERTa-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Domain-RoBERTa-large Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Gemma-2-2B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-Gemma-2-9B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-General-BERT-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-General-Longformer-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-General-Longformer-large Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-General-RoBERTa-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-General-RoBERTa-large Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Llama-3.1-8B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-Llama-3.2-3B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-Llama-3.3-70B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-Mistral-Nemo-12B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-Mixed-BERT-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Mixed-Longformer-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Mixed-Longformer-large Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Mixed-RoBERTa-base Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Mixed-RoBERTa-large Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T22-Phi-4-14B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T22-Qwen-2.5-14B Task 22: Pancreatic ductal adenocarcinoma size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-DeepSeek-R1-14B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-Domain-BERT-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Domain-Longformer-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Domain-Longformer-large Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Domain-RoBERTa-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Domain-RoBERTa-large Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Gemma-2-2B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-Gemma-2-9B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-General-BERT-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-General-Longformer-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-General-Longformer-large Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-General-RoBERTa-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-General-RoBERTa-large Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Llama-3.1-8B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-Llama-3.2-3B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-Llama-3.3-70B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-Mistral-Nemo-12B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-Mixed-BERT-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Mixed-Longformer-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Mixed-Longformer-large Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Mixed-RoBERTa-base Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Mixed-RoBERTa-large Task 23: Pulmonary nodule size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T23-Phi-4-14B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T23-Qwen-2.5-14B Task 23: Pulmonary nodule size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-DeepSeek-R1-14B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-Domain-BERT-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Domain-Longformer-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Domain-Longformer-large Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Domain-RoBERTa-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Domain-RoBERTa-large Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Gemma-2-2B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-Gemma-2-9B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-General-BERT-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-General-Longformer-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-General-Longformer-large Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-General-RoBERTa-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-General-RoBERTa-large Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Llama-3.1-8B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-Llama-3.2-3B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-Llama-3.3-70B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-Mistral-Nemo-12B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-Mixed-BERT-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Mixed-Longformer-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Mixed-Longformer-large Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Mixed-RoBERTa-base Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Mixed-RoBERTa-large Task 24: RECIST lesion size measurement Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T24-Phi-4-14B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T24-Qwen-2.5-14B Task 24: RECIST lesion size measurement Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-DeepSeek-R1-14B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-Domain-BERT-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Domain-Longformer-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Domain-Longformer-large Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Domain-RoBERTa-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Domain-RoBERTa-large Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Gemma-2-2B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-Gemma-2-9B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-General-BERT-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-General-Longformer-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-General-Longformer-large Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-General-RoBERTa-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-General-RoBERTa-large Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Llama-3.1-8B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-Llama-3.2-3B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-Llama-3.3-70B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-Mistral-Nemo-12B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-Mixed-BERT-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Mixed-Longformer-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Mixed-Longformer-large Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Mixed-RoBERTa-base Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Mixed-RoBERTa-large Task 26: Medical terminology recognition Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T26-Phi-4-14B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T26-Qwen-2.5-14B Task 26: Medical terminology recognition Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-DeepSeek-R1-14B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-Domain-BERT-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Domain-Longformer-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Domain-Longformer-large Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Domain-RoBERTa-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Domain-RoBERTa-large Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Gemma-2-2B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-Gemma-2-9B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-General-BERT-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-General-Longformer-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-General-Longformer-large Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-General-RoBERTa-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-General-RoBERTa-large Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Llama-3.1-8B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-Llama-3.2-3B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-Llama-3.3-70B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-Mistral-Nemo-12B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-Mixed-BERT-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Mixed-Longformer-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Mixed-Longformer-large Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Mixed-RoBERTa-base Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Mixed-RoBERTa-large Task 27: Prostate biopsy sampling Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T27-Phi-4-14B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T27-Qwen-2.5-14B Task 27: Prostate biopsy sampling Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-DeepSeek-R1-14B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-Domain-BERT-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Domain-Longformer-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Domain-Longformer-large Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Domain-RoBERTa-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Domain-RoBERTa-large Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Gemma-2-2B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-Gemma-2-9B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-General-BERT-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-General-Longformer-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-General-Longformer-large Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-General-RoBERTa-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-General-RoBERTa-large Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Llama-3.1-8B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-Llama-3.2-3B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-Llama-3.3-70B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-Mistral-Nemo-12B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-Mixed-BERT-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Mixed-Longformer-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Mixed-Longformer-large Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Mixed-RoBERTa-base Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Mixed-RoBERTa-large Task 28: Skin histopathology diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T28-Phi-4-14B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T28-Qwen-2.5-14B Task 28: Skin histopathology diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-DeepSeek-R1-14B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-Domain-BERT-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Domain-Longformer-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Domain-Longformer-large Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Domain-RoBERTa-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Domain-RoBERTa-large Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Gemma-2-2B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-Gemma-2-9B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-General-BERT-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-General-Longformer-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-General-Longformer-large Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-General-RoBERTa-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-General-RoBERTa-large Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Llama-3.1-8B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-Llama-3.2-3B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-Llama-3.3-70B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-Mistral-Nemo-12B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-Mixed-BERT-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Mixed-Longformer-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Mixed-Longformer-large Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Mixed-RoBERTa-base Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Mixed-RoBERTa-large Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T3-Phi-4-14B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T3-Qwen-2.5-14B Task 3: Kidney abnormality identification, Classify whether any form of kidney abnormality is men2oned in the radiology report Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-DeepSeek-R1-14B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-Domain-BERT-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Domain-Longformer-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Domain-Longformer-large Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Domain-RoBERTa-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Domain-RoBERTa-large Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Gemma-2-2B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-Gemma-2-9B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-General-BERT-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-General-Longformer-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-General-Longformer-large Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-General-RoBERTa-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-General-RoBERTa-large Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Llama-3.1-8B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-Llama-3.2-3B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-Llama-3.3-70B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-Mistral-Nemo-12B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-Mixed-BERT-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Mixed-Longformer-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Mixed-Longformer-large Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Mixed-RoBERTa-base Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Mixed-RoBERTa-large Task 4: Skin histopathology case selection Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T4-Phi-4-14B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T4-Qwen-2.5-14B Task 4: Skin histopathology case selection Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-DeepSeek-R1-14B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-Domain-BERT-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Domain-Longformer-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Domain-Longformer-large Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Domain-RoBERTa-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Domain-RoBERTa-large Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Gemma-2-2B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-Gemma-2-9B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-General-BERT-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-General-Longformer-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-General-Longformer-large Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-General-RoBERTa-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-General-RoBERTa-large Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Llama-3.1-8B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-Llama-3.2-3B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-Llama-3.3-70B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-Mistral-Nemo-12B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-Mixed-BERT-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Mixed-Longformer-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Mixed-Longformer-large Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Mixed-RoBERTa-base Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Mixed-RoBERTa-large Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T5-Phi-4-14B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T5-Qwen-2.5-14B Task 5: RECIST timeline, Predict on a report level whether this is a baseline or follow-up scan, Provide your predic2on as\na confidence score between 0 and 1, where 0 corresponds to False (follow-up report) and 1\ncorresponds to True (baseline report). Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-DeepSeek-R1-14B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-Domain-BERT-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Domain-Longformer-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Domain-Longformer-large Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Domain-RoBERTa-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Domain-RoBERTa-large Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Gemma-2-2B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-Gemma-2-9B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-General-BERT-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-General-Longformer-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-General-Longformer-large Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-General-RoBERTa-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-General-RoBERTa-large Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Llama-3.1-8B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-Llama-3.2-3B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-Llama-3.3-70B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-Mistral-Nemo-12B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-Mixed-BERT-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Mixed-Longformer-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Mixed-Longformer-large Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Mixed-RoBERTa-base Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Mixed-RoBERTa-large Task 6: Histopathology cancer origin Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T6-Phi-4-14B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T6-Qwen-2.5-14B Task 6: Histopathology cancer origin Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-DeepSeek-R1-14B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-Domain-BERT-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Domain-Longformer-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Domain-Longformer-large Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Domain-RoBERTa-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Domain-RoBERTa-large Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Gemma-2-2B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-Gemma-2-9B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-General-BERT-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-General-Longformer-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-General-Longformer-large Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-General-RoBERTa-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-General-RoBERTa-large Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Llama-3.1-8B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-Llama-3.2-3B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-Llama-3.3-70B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-Mistral-Nemo-12B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-Mixed-BERT-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Mixed-Longformer-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Mixed-Longformer-large Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Mixed-RoBERTa-base Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Mixed-RoBERTa-large Task 7: Pulmonary nodule size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T7-Phi-4-14B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T7-Qwen-2.5-14B Task 7: Pulmonary nodule size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-DeepSeek-R1-14B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-Domain-BERT-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Domain-Longformer-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Domain-Longformer-large Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Domain-RoBERTa-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Domain-RoBERTa-large Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Gemma-2-2B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-Gemma-2-9B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-General-BERT-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-General-Longformer-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-General-Longformer-large Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-General-RoBERTa-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-General-RoBERTa-large Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Llama-3.1-8B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-Llama-3.2-3B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-Llama-3.3-70B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-Mistral-Nemo-12B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-Mixed-BERT-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Mixed-Longformer-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Mixed-Longformer-large Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Mixed-RoBERTa-base Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Mixed-RoBERTa-large Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T8-Phi-4-14B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T8-Qwen-2.5-14B Task 8: Pancreatic ductal adenocarcinoma size presence Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-DeepSeek-R1-14B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-Domain-BERT-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Domain-Longformer-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Domain-Longformer-large Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Domain-RoBERTa-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Domain-RoBERTa-large Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Gemma-2-2B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-Gemma-2-9B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-General-BERT-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-General-Longformer-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-General-Longformer-large Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-General-RoBERTa-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-General-RoBERTa-large Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Llama-3.1-8B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-Llama-3.2-3B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-Llama-3.3-70B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-Mistral-Nemo-12B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-Mixed-BERT-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Mixed-Longformer-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Mixed-Longformer-large Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Mixed-RoBERTa-base Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Mixed-RoBERTa-large Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
T9-Phi-4-14B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
T9-Qwen-2.5-14B Task 9: Pancreatic ductal adenocarcinoma diagnosis Information extraction No Yes Builtjes 2025 Leveraging open-source large language models for clinical information extraction in resource-constrained settings link
Vandenbussche-All-LR Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-All-NB Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-All-SVM Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-Ngrams-LR Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-Ngrams-Meta-LR Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-Ngrams-Meta-NB Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-Ngrams-Meta-SVM Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-Ngrams-NB Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Vandenbussche-Ngrams-SVM Classifying self-reported narratives in either migraine or cluster headache Information extraction No Yes Vandenbussche 2022 Using natural language processing to automatically classify written self-reported narratives by patients with migraine or cluster headache
Verschueren-CTCue-rule-based capturing disease progression in electronic health\nrecord (EHR) data of patients with metastatic non– small cell lung cancer (mNSCLC) treated with immunochemotherapy. Information extraction Yes No Verschueren 2024 Development and Portability of a Text Mining Algorithm for Capturing Disease Progression in Electronic Health Records of Patients With Stage IV Non–Small Cell Lung Cancer
Wasylewicz-TM extraction of the occurrence and severity of eleven commonly described cardiac characteristics Information extraction No Yes Wasylewicz 2021 Identifying adverse drug reactions from free-text electronic hospital health record notes
belabBERT-HAGA lifestyle characteristics classification, specifically on smoking, alcohol and drug usage Information extraction No Yes Muizelaar 2024 Extracting patient lifestyle characteristics from Dutch clinical text with BERT models link
Fivez-W2V-Context Language normalization, representation, and modeling No Yes Fivez 2017 Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings link
Fivez-W2V-NoisyChannel Language normalization, representation, and modeling No Yes Fivez 2017 Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings link
Marin-AWD-LSTM Language normalization, representation, and modeling No Yes Marin 2020 Effectiveness of neural language models for word prediction of textual mammography reports link
Marin-FRAGE-LSTM Language normalization, representation, and modeling No Yes Marin 2020 Effectiveness of neural language models for word prediction of textual mammography reports link
Marin-LSTM Language normalization, representation, and modeling No Yes Marin 2020 Effectiveness of neural language models for word prediction of textual mammography reports link
MedRoBERTa.nl Language normalization, representation, and modeling Yes Yes Verkijk 2021 MedRoBERTa.nl: A Language Model for Dutch Electronic Health Records link
joeranbosma/dragon-bert-base-domain-specific Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-bert-base-mixed-domain Language normalization, representation, and modeling Yes No Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-longformer-base-domain-specific\t Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-longformer-base-mixed-domain Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-longformer-large-domain-specific Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-longformer-large-mixed-domain Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-roberta-base-domain-specific Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-roberta-base-mixed-domain\t Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-roberta-large-domain-specific\t Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link
joeranbosma/dragon-roberta-large-mixed-domain Language normalization, representation, and modeling Yes Yes Bosma 2025 The DRAGON benchmark for clinical NLP link