Resources


Database Credentialed Access

Deidentified Medical Text

Margaret Douglass, Bill Long, George Moody, Peter Szolovits, Li-wei Lehman, Roger Mark, Gari D. Clifford

Gold standard corpus of 2,434 deidentified nursing notes

medical text nursing notes hipaa de-identification

Published: Dec. 18, 2007. Version: 1.0

Database Credentialed Access

Deidentified Medical Text

Margaret Douglass, Bill Long, George Moody, Peter Szolovits, Li-wei Lehman, Roger Mark, Gari D. Clifford

Gold standard corpus of 2,434 deidentified nursing notes

medical text nursing notes hipaa de-identification

Published: Dec. 18, 2007. Version: 1.0

Database Credentialed Access

EHR-DS-QA: A Synthetic QA Dataset Derived from Medical Discharge Summaries for Enhanced Medical Information Retrieval Systems

Konstantin Kotschenreuther

Dataset consisting of question and answer pairs synthetically generated from medical discharge summaries, designed to facilitate the training and development of large language models specifically tailored for healthcare applications

mimic-iv clinical question-answering medical discharge summaries large language models

Published: Jan. 11, 2024. Version: 1.0.0

Database Credentialed Access

Medical Expert Annotations of Unsupported Facts in Doctor-Written and LLM-Generated Patient Summaries

Stefan Hegselmann, Shannon Shen, Florian Gierse, Monica Agrawal, David Sontag, Xiaoyi Jiang

Annotations for unsupported facts in 100 original MIMIC patient summaries (discharge instructions) and hallucinations in 100 Large Language Model (LLM) generated patient summaries labeled by two medical experts.

Published: April 28, 2024. Version: 1.0.0

Database Credentialed Access

RuMedNLI: A Russian Natural Language Inference Dataset For The Clinical Domain

Pavel Blinov, Aleksandr Nesterov, Galina Zubkova, Arina Reshetnikova, Vladimir Kokh, Chaitanya Shivade

RuMedNLI is the full counterpart dataset of MedNLI in Russian language.

natural language inference recognizing textual entailment russian language

Published: April 1, 2022. Version: 1.0.0

Database Credentialed Access

FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark

Mingjie Li, Wenjia Cai, Rui Liu, Yuetian Weng, Xiaoyun Zhao, Cong Wang, Xin Chen, Zhong Liu, Caineng Pan, Mengke Li, Yingfeng Zheng, Yizhi Liu, Flora Salim, Karin Verspoor, Xiaodan Liang, Xiaojun Chang

Benchmark dataset for report generation based on fundus fluorescein angiography images and reports.

fundus fluorescein angiography explainable and reliable evaluation vision and language medical report generation

Published: Sept. 21, 2021. Version: 1.0.0

Software Open Access

De-Identification Software Package

The deid software package includes code and dictionaries for automated location and removal of protected health information (PHI) in free text from medical records.

phi deidentification anonymization

Published: Dec. 18, 2007. Version: 1.1

Database Credentialed Access

Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database

Xiang Yue, Xinliang Frederick Zhang, Huan Sun

Annotated Question Answering Pairs for Clinical Notes in the MIMIC-III Database

clinical question answering clinical nlp clinical reading comprehension

Published: Jan. 15, 2021. Version: 1.0.0

Database Credentialed Access

MIMIC-IV-Ext-BHC: Labeled Clinical Notes Dataset for Hospital Course Summarization

Asad Aali, Dave Van Veen, Yamin Arefeen, Jason Hom, Christian Bluethgen, Eduardo Pontes Reis, Sergios Gatidis, Namuun Clifford, Joseph Daws, Arash Tehrani, Jangwon Kim, Akshay Chaudhari

This dataset presents a collection of preprocessed and labeled clinical notes derived from "MIMIC-IV-Note", and aims to facilitate the development of ML models focused on summarizing brief hospital courses (BHC) from clinical notes.

natural language processing clinical notes brief hospital course text summarization machine learning

Published: Oct. 10, 2024. Version: 1.1.0

Database Credentialed Access

Learning to Ask Like a Physician: a Discharge Summary Clinical Questions (DiSCQ) Dataset

Eric Lehman

Dataset of questions asked by medical experts about patients. Medical experts will read a discharge summary line-by-line and (1) ask any question that they may have and (2) record what in the text "triggered" them to ask their question.

question generation question answering machine learning

Published: July 28, 2022. Version: 1.0