Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning (CRITICAL)

Author ORCID

Yuan Luo 0000-0003-0195-7456

Luke Rasmussen 0000-0002-4497-8049

Andrew Williams 0000-0002-0692-412X

Jared Houghtaling 0000-0003-1557-9716

Andrew Michelson 0000-0001-8112-9516

Philip Payne 0000-0002-9532-2998

James Cimino 0000-0003-4101-1622

John Osborne 0000-0002-0851-1150

Yikuan Li 0000-0001-7546-9979

Saki Amagai 0000-0002-2825-4219

Meghan Hutch 0000-0001-8130-9429

Jenny Ding 0009-0007-1437-9719

Adrienne Kline 0000-0002-0052-0685

Catherine A. Gao 0000-0001-5576-3943

Rui Zhu 0009-0004-4286-7398

Matthew Wyatt 0000-0002-1632-7556

Snehil Gupta 0000-0001-5163-2196

Aditi Gupta 0000-0002-4839-9271

Weiwei Ma 0000-0002-3241-7607

Patrick Cannon 0000-0002-3682-6221

Justin Starren 0000-0002-5403-1115

Kristi Holmes 0000-0001-8420-5254

Matthew Carson 0000-0003-4105-9220

Albert Lai 0000-0002-9241-2656

Adam Wilcox 0000-0002-6305-735X

Publication Date

12-19-2024

Abstract

The CRITICAL dataset is the first cross-Clinical and Translational Science Award (CTSA) initiative to create a multi-site, multi-modal, de-identified clinical dataset. It combines deep-data depth with broad-data width, addressing a major unmet need in healthcare research. The dataset encompasses comprehensive longitudinal inpatient and outpatient data, including pre-, during- and post-ICU admissions, for approximately 400,000 distinct critical-care patients. This diverse dataset supports the exploration of urgent clinical problems and facilitates the development of fair and generalizable AI tools for advanced patient monitoring and decision support. The dataset has been curated to serve the research community, fostering innovations in AI/machine learning (ML), outcomes research, and other translational science domains. Its unique combination of size, diversity, and comprehensiveness makes it a valuable resource for tackling long-standing clinical challenges. The metadata provided here for this dataset is licensed under Attribution 4.0 International (CC BY 4.0). Please note that access to the data itself is restricted and requires compliance with the applicable Data Use Agreement (DUA). More information about the DUA and how to request access can be found at https://critical.fsm.northwestern.edu/, or by contacting critical@northwestern.edu.

Keywords

Critical Care, Big Data, Artificial Intelligence

Repository

Zenodo

Distribution License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Funder

Funder: National Center for Advancing Translational Sciences
Funder DOI: 10.13039/100006108
CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning
U01TR003528

Link to Dataset

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