# CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning

> **NIH NIH U01** · NORTHWESTERN UNIVERSITY · 2022 · $1,202,957

## Abstract

CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics,
Comprehensive Analytics, and Learning
Translational research in Artificial Intelligence (AI) has been hindered by the lack of shared data resources with
sufficient depth, breadth and diversity. There are very limited EHR datasets freely available to the general
research community especially the AI research community through credential-based access. MIMIC dataset is
from a single institution that has a fixed and limited racial, ethnic and geographic profile. The eICU dataset is
limited in data comprehensiveness (e.g., number of kinds of lab tests ~1/5 of MIMIC), data span (1 year, 2014-
2015), and data variety (e.g., no free text clinical notes) etc. Thus MIMIC and eICU respectively have advantages
and disadvantages of data depth and data breadth.
The vision of this proposal is to leverage multiple CTSAs with diverse racial, ethnic and geographic profiles in
order to develop and evaluate a multi-site de-identified ICU dataset, to facilitate accelerate translational research
in AI and deep learning approaches to understand, track, and predict the pathophysiological state of patients. In
this project, a group of nationwide CTSA sites will work together to build a new, more inclusive, multi-site dataset
that is downloadable from NCATS cloud by researchers with credential-based access. This project will combine
the respective advantages of MIMIC (data depth) and eICU (data breadth). The created dataset will include more
geographic regions, larger quantities of time-series data, including pre-, during- and post- ICU patient
information. This will incorporate not only more patient diversity, but also capture regional population differences
and practice variations that could have clinical impact.
Aim 1 will develop and provide credentialed access to a multi-site dataset consisting of de-identified discrete
outpatient, inpatient, and ICU data for critically ill at respective CTSAs. Aim 2 will create federated access dataset
from and develop novel federated learning methods on the part of the multi-site ICU data consisting of
unstructured clinical notes or structured data for select group of patients at higher risks of re-identification (e.g.,
rare disease patients). Aim 3 will develop novel memory-network based meta-learning AI algorithms and use the
multi-site dataset to answer concrete and long-standing clinical problems in critical care. Aim 4 will innovatively
leverage the library network to develop and disseminate open resources for the research community and develop
best practice guidelines for other CTSAs to join the effort. In particular, we aim to support and cultivate the growth
of next generation medical AI workforce for research and practice.
We aim to establish a large cross-CTSA collaborative data sharing for critical care by leveraging the existing
CTSA collaborative networks. With the diversified racial, ethnic and geographic profiles from the above CTS...

## Key facts

- **NIH application ID:** 10461229
- **Project number:** 5U01TR003528-02
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** JAMES J CIMINO
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,202,957
- **Award type:** 5
- **Project period:** 2021-08-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10461229

## Citation

> US National Institutes of Health, RePORTER application 10461229, CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning (5U01TR003528-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10461229. Licensed CC0.

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