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AI for Clinical Care
Creating a patient-focused collaborative hospital repository uniting standards for clinical care AI
Expanding Artificial Intelligence and Machine Learning to Improve Recovery from Acute Illness
The CHoRUS project is one data generation project of four in the National Institute of Health (NIH) funded Bridge2AI consortium. The goal is to work with projects to create ethically sourced datasets and tools around Artificial Intelligence and to create best practices for AI in health.
Snapshot of the dataset
Anticipated Final Dataset
100,000
Patient admissions
9
Different data modalities
14
Data contributing hospitals
60+
CHoRUS consortium members across 20 different institutions
Current Released Dataset
50,000
Patient admissions from ICU, PICU, and NICU
1.6 Billion
Rows of EHR OMOP data
7,642
Admissions with Radiology Data
23 Tb
Waveform data
CHoRUS Project Components
Data
The CHoRUS project is the collection of multi-modal data across 14 different hospitals. The data utilizes ongoing and new standardization practices. The team creates tools to support the cloud enclave.
Ethics
The CHoRUS consortium evaluates community perspectives on clinical care AI to find ways to increase trustworthiness of provenance and privacy in AI. New approaches are being created as existing legal and regulatory landscape is analyzed.
People
Dedicated to engaging the community in CHoRUS and Bridge2AI work products, CHoRUS project is dedicated to training engineers, data scientists, and clinicians to solidify AI in Clinical Care Data Engineering Infrastructure.
Learning Opportunities
Learn of different opportunities to gain hands-on training using the CHoRUS dataset to support the development of transnational AI in clinical care.
CHoRUS Consortium
