Resources

GOSSIS-1 is a modern, free, open-source in-hospital mortality prediction algorithm for critical care patients, achieving excellent discrimination and calibration across three countries (Australia, New Zealand and the USA). GOSSIS-1 was developed on …

This project is a demo version of the eICU Collaborative Research Database, a multi-center database comprised of deidentified health data for over 200,000 admissions to ICUs across the United States between 2014-2015. The database includes vital sig…

WiDS (Women in Data Science) Datathon 2020: ICU Mortality Prediction focuses on patient health through data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative. Brought to you by the Global WiDS team, the West Big Data Innova…

The eICU Collaborative Research Database is a multi-center database comprising deidentified health data associated with over 200,000 admissions to ICUs across the United States between 2014-2015. The database includes vital sign measurements, care p…

MIMIC-III-Ext-PPG is a large-scale, annotated, and quality-assessed dataset of photoplethysmogram (PPG) segments extracted from the MIMIC-III Waveform Database Matched Subset. It contains around 6.3 million non-overlapping 30-second segments from 6,…

Immunosuppression due to underlying conditions or immunosuppressive medication use increases the risk of morbidity and mortality in the context of infectious disease. Identifying patients with immunosuppression is important for better studying and u…

Accurate medical decision-making is critical for both patients and clinicians. Patients often find it difficult to interpret their symptoms, determine their severity, and select the right specialist to see. At the same time, clinicians face challeng…