Resources


Database Open Access

MIMIC-IV Clinical Database Demo on FHIR

Alex Bennett, Joshua Wiedekopf, Hannes Ulrich, Alistair Johnson

MIMIC-IV-on-FHIR is a hundred patient demo of MIMIC-IV v2.0 in the Fast Healthcare Interoperability Resources(FHIR) format. MIMIC-IV-on-FHIR provides implementers with a real-world FHIR datastore to aid in FHIR research and development.

fhir mimic electronic health records

Published: June 7, 2022. Version: 2.0

Database Credentialed Access

DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

Jayetri Bardhan, Anthony Colas, Kirk Roberts, Daisy Zhe Wang

DrugEHRQA is a QA dataset containing question-answers from MIMIC-III tables and discharge summaries.

question-answer qa

Published: April 12, 2022. 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

RadNLI: A natural language inference dataset for the radiology domain

Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, Dan Jurafsky

A radiology NLI dataset introduced in the paper: Improving Factual Completeness and Consistency of Image-to-text Radiology Report Generation

Published: June 29, 2021. Version: 1.0.0

Database Credentialed Access

RadGraph: Extracting Clinical Entities and Relations from Radiology Reports

Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Matthew Lungren, Andrew Ng, Curtis Langlotz, Pranav Rajpurkar

RadGraph is a dataset of entities and relations in full-text chest X-ray radiology reports, which are obtained using a novel information extraction (IE) schema to capture clinically relevant information in a radiology report.

entity and relation extraction graph multi-modal natural language processing radiology

Published: June 3, 2021. Version: 1.0.0

Database Restricted Access

Pulmonary Edema Severity Grades Based on MIMIC-CXR

Ruizhi Liao, Geeticka Chauhan, Polina Golland, Seth Berkowitz, Steven Horng

Pulmonary edema metadata and labels for MIMIC-CXR

Published: Feb. 9, 2021. Version: 1.0.1

Database Open Access

NInFEA: Non-Invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research

Danilo Pani, Eleonora Sulas, Monica Urru, Reza Sameni, Luigi Raffo, Roberto Tumbarello

Open dataset featuring non-invasive electrophysiological recordings, fetal pulsed-wave Doppler and maternal respiration signals. It provides a ground truth on the fetal heart activity when an invasive scalp lead is unavailable.

foetus pwd doppler foetal ecg maternal ecg pwd envelope non-invasive cardiology early pregnancy antenatal fecg ecg

Published: Nov. 12, 2020. Version: 1.0.0

Visualize waveforms

Database Open Access

PTB Diagnostic ECG Database

ECGs obtained from 290 subjects using a non-commercial, prototype recorder developed at Physikalisch-Technische Bundesanstalt.

ecg

Published: Sept. 25, 2004. Version: 1.0.0

Visualize waveforms

Database Credentialed Access

GOSSIS-1-eICU, the eICU-CRD subset of the Global Open Source Severity of Illness Score (GOSSIS-1) dataset

Jesse Raffa, Alistair Johnson, Tom Pollard, Omar Badawi

GOSSIS-1 is an in-hospital mortality prediction algorithm for critical care patients. GOSSIS-1 was trained using data from three countries. This dataset corresponds with the USA subset of the GOSSIS-1 dataset for the 2022 publication below.

icu critical care severity of illness global gossis apache mortality prediction benchmarking

Published: July 20, 2022. Version: 1.0.0

Database Credentialed Access

GOSSIS-1-eICU, the eICU-CRD subset of the Global Open Source Severity of Illness Score (GOSSIS-1) dataset

Jesse Raffa, Alistair Johnson, Tom Pollard, Omar Badawi

GOSSIS-1 is an in-hospital mortality prediction algorithm for critical care patients. GOSSIS-1 was trained using data from three countries. This dataset corresponds with the USA subset of the GOSSIS-1 dataset for the 2022 publication below.

icu critical care severity of illness global gossis apache mortality prediction benchmarking

Published: July 20, 2022. Version: 1.0.0