Resources


Software Open Access

Transformer-DeID: Deidentification of free-text clinical notes with transformers

Callandra Moore, Lucas Bulgarelli, Tom Pollard, Alistair Johnson

Fine tune transformer-based neural networks to deidentify clinical text data.

deidentification neural networks transformers

Published: Nov. 2, 2023. Version: 1.0.0

Software Open Access

Transformer-DeID: Deidentification of free-text clinical notes with transformers

Callandra Moore, Lucas Bulgarelli, Tom Pollard, Alistair Johnson

Fine tune transformer-based neural networks to deidentify clinical text data.

deidentification neural networks transformers

Published: Nov. 2, 2023. Version: 1.0.0

Model Credentialed Access

Transformer models trained on MIMIC-III to generate synthetic patient notes

Ali Amin-Nejad, Julia Ive, Sumithra Velupillai

Machine learning models that have been trained using MIMIC-III to enable the creation of synthetic discharge summaries.

Published: May 27, 2020. Version: 1.0.0

Database Open Access

MIMIC-IV demo data in the OMOP Common Data Model

Michael Kallfelz, Anna Tsvetkova, Tom Pollard, Manlik Kwong, Gigi Lipori, Vojtech Huser, Jeffrey Osborn, Sicheng Hao, Andrew Williams

Preliminary work to transform a MIMIC-IV demo dataset to the OMOP Common Data Model

omop common data model

Published: June 21, 2021. Version: 0.9

Database Credentialed Access

MedVAL-Bench: Expert-Annotated Medical Text Validation Benchmark

Asad Aali, Vasiliki Bikia, Maya Varma, Nicole Chiou, Sophie Ostmeier, Arnav Singhvi, Magdalini Paschali, Ashwin Kumar, Andrew Johnston, Karimar Amador Martinez, Eduardo Perez Guerrero, Paola Cruz Rivera, Sergios Gatidis, Christian Bluethgen, Eduardo Pontes Reis, Eddy Zandee van Rilland, Poonam Hosamani, Kevin Keet, Minjoung Go, Evelyn Ling, David Larson, Curtis Langlotz, Roxana Daneshjou, Jason Hom, Sanmi Koyejo, Emily Alsentzer, Akshay Chaudhari

MedVAL-Bench is the first large-scale physician-validated benchmark for medical text validation, spanning 6 diverse medical tasks and containing 840 language model-generated outputs annotated by 12 physicians with error assessments and risk grades.

Published: Nov. 14, 2025. Version: 1.0.1

Database Credentialed Access

RaDialog Instruct Dataset

Chantal Pellegrini, Ege Özsoy, Benjamin Busam, Nassir Navab, Matthias Keicher

Image-based instruct data for Chest X-Ray understanding and analysis.

medical image understaning radiology chatbot radiology report generation radiology assistant large vision-language models

Published: July 12, 2024. Version: 1.1.0

Database Open Access

PADS - Parkinsons Disease Smartwatch dataset

Julian Varghese, Alexander Brenner, Lucas Plagwitz, Catharina van Alen, Michael Fujarski, Tobias Warnecke

The PADS dataset contains smartwatch-based records from interactive neurological assessments of Parkinsons disease patients, differential diagnoses and healthy controls. The data is complemented with non-motor symptoms and medical history information

wearables movement disorders parkinsons disease

Published: March 25, 2024. Version: 1.0.0

Database Credentialed Access

EchoNotes Structured Database derived from MIMIC-III (ECHO-NOTE2NUM)

Gloria Hyunjung Kwak, Dana Moukheiber, Mira Moukheiber, Lama Moukheiber, Sulaiman Moukheiber, Neel Butala, Leo Anthony Celi, Christina Chen

A structured echocardiogram database derived from 43,472 observational notes obtained during echocardiogram studies conducted in the intensive care unit at the Beth Israel Deaconess Medical Center between 2001 and 2012.

Published: Feb. 23, 2024. Version: 1.0.0

Database Credentialed Access

RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports

Sarvesh Soni, Kirk Roberts

RadQA is an electronic health record question answering dataset containing clinical questions that can be answered using the Findings and Impressions sections of radiology reports

machine reading comprehension radiology reports question answering clinical notes electronic health records

Published: Dec. 9, 2022. Version: 1.0.0

Model Credentialed Access

EntityBERT: BERT-based Models Pretrained on MIMIC-III with or without Entity-centric Masking Strategy for the Clinical Domain

Chen Lin, Steven Bethard, Guergana Savova, Timothy Miller, Dmitriy Dligach

Pretraining of models with a broad representation of biomedical terminology (PubMedBERT) on MIMIC-III corpus along with or without a novel entity-centric masking strategy.

Published: March 17, 2022. Version: 1.0.1