Resources


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

MIMIC-III - SequenceExamples for TensorFlow modeling

Jonas Kemp, Kun Zhang, Andrew Dai

MIMIC-III data converted into TensorFlow SequenceExample format, for use in modeling pipelines.

tensorflow sequence modeling machine learning deep learning

Published: Sept. 29, 2020. Version: 1.0.0

Software Open Access

Model for Simulating ECG and PPG Signals with Arrhythmia Episodes

Andrius Sološenko, Andrius Petrėnas, Birutė Paliakaitė, Vaidotas Marozas, Leif Sörnmo

A model is capable of simulating sinus rhythm, atrial fibrillation and ectopic beats in ECGs and PPGs as well as extreme bradycardia and ventricular tachycardia in PPGs. Different types of noises and artifacts can also be added to the waveforms.

arrhythmia atrial fibrillation noise tachycardia detection motion artifacts simulation bradycardia ppg ecg

Published: May 2, 2022. Version: 1.3.1

Software Open Access

AFVP - A Realistic Ventricular Rhythm Model During AF

AFVP generates a synthesized beat-to-beat interval sequence of ventricular excitations with a realistic structure observed during AF.

interbeat rr interval simulation

Published: Aug. 9, 2007. Version: 1.0.0

Challenge Open Access

RR Interval Time Series Modeling: The PhysioNet/Computing in Cardiology Challenge 2002

We are pleased to announce the third in our annual series of challenges from PhysioNet and Computers in Cardiology. We received many suggestions for challenge topics, and encourage you to write to us with further suggestions. We chose the topic for…

interbeat rr interval challenge

Published: Feb. 14, 2002. Version: 1.0.0

Model Credentialed Access

Shareable Artificial Intelligence to Extract Cancer Outcomes from Electronic Health Records for Precision Oncology Research

Kenneth Kehl, Pavel Trukhanov, Christopher Fong, Justin Jee, Karl Pichotta, Morgan Paul, Chelsea Nichols, Michele Waters, Nikolaus Schultz, Deborah Schrag

The DFCI-imaging-student and DFCI-medonc-student AI models for extracting cancer outcomes from imaging reports and medical oncologist notes from electronic health records.

Published: Oct. 24, 2024. Version: 1.0.0

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 Restricted Access

MIMIC-IV-Ext-Apixaban-Trial-Criteria-Questions

Elizabeth Woo, Michael Craig Burkhart, Emily Alsentzer, Brett Beaulieu-Jones

We created 23 questions resembling eligibility criteria from the apixaban clinical trial and evaluated them on a random sample of 100 patient notes from MIMIC-IV. We release the 2300 total question-answer pairs as a dataset here.

clinical q and a evaluation set clinical trial eligibility

Published: April 30, 2025. Version: 1.0.0

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

ReXPref-Prior: A MIMIC-CXR Preference Dataset for Reducing Hallucinated Prior Exams in Radiology Report Generation

Oishi Banerjee, Hong-Yu Zhou, Subathra Adithan, Stephen Kwak, Kay Wu, Pranav Rajpurkar

We propose ReXPref-Prior, an adapted version of MIMIC-CXR where GPT-4 has removed references to prior exams from both findings and impression sections of chest X-ray reports.

chest x-rays reinforcement learning hallucination

Published: Aug. 14, 2024. Version: 1.0.0