Resources


Software Open Access

Measurement of Global Electrical Heterogeneity

The Global Electrical Heterogeneity (GEH) concept is based on the theory of Wilson’s electrical gradient vector, which characterizes the degree of heterogeneity of the total recovery time across the ventricles.

Published: May 2, 2018. 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

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

Challenge Restricted Access

WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction

Meredith Lee, Jesse Raffa, Marzyeh Ghassemi, Tom Pollard, Sharada Kalanidhi, Omar Badawi, Karen Matthys, Leo Anthony Celi

WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction focuses on patient health. Join a team, explore the data, and share your insights: http://bit.ly/WiDSdatathon2020

mortality risk data science kaggle icu challenge predictive analytics women in data science

Published: Jan. 22, 2020. Version: 1.0.0

Database Open Access

Patient-level dataset to study the effect of COVID-19 in people with Multiple Sclerosis

Hamza Khan, Lotte Geys, peer baneke, Giancarlo Comi, Liesbet Peeters

This dataset is part of the Global Data Sharing Initiative. The data was acquired by people with MS and clinicians using a fast data entry tool. The dataset includes demographics, comorbidities and hospital stay and COVID-19 symptoms of PwMS.

Published: Jan. 2, 2024. Version: 1.0.1

Database Credentialed Access

Chest ImaGenome Dataset

Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, Mehdi Moradi

The Chest ImaGenome dataset is a scene graph dataset with additional chronological comparison relations for chest X-rays. It is automatically derived from the MIMIC-CXR dataset. A manually annotated gold standard is also available for 500 patients.

multimodal machine learning chest x-ray radiology scene graph visual dialogue object detection semantic reasoning bounding box relation extraction knowledge graph explainability reasoning chest cxr visual question answering deep learning disease progression

Published: July 13, 2021. Version: 1.0.0

Database Credentialed Access

VinDr-CXR: An open dataset of chest X-rays with radiologist annotations

Ha Quy Nguyen, Hieu Huy Pham, le tuan linh, Minh Dao, lam khanh

VinDr-CXR: An open dataset of chest X-rays with radiologist's annotations

lesion detection disease classification chest x-ray interpretation computer vision deep learning

Published: June 22, 2021. Version: 1.0.0

Software Open Access

Heart Vector Origin Point Detection and Time-Coherent Median Beat Construction

Erick Andres Perez Alday, Larisa Tereshchenko

The algorithm finds the heart vector origin point and constructs the time-coherent median beat. VCG origin point is defined as the electrically quiet or isoelectric state of the heart when the heart vector does not move in 3D space.

baseline vectorcardiogram origin point heart vector signal processing electrocardiogram

Published: May 25, 2021. Version: 1.0.0

Challenge Open Access

Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019

Matthew Reyna, Chris Josef, Russell Jeter, Supreeth Shashikumar, Benjamin Moody, M. Brandon Westover, Ashish Sharma, Shamim Nemati, Gari D. Clifford

The 2019 PhysioNet Computing in Cardiology Challenge invites participants to predict sepsis in clinical data

prediction sepsis challenge

Published: Aug. 5, 2019. Version: 1.0.0