“The database, which includes patient information from 2020 and 2021, now contains significant overlap with the Covid-19 pandemic, yielding valuable patient data for research,” said Leo Anthony Celi, principal research scientist and clinical research director at the Laboratory of Computational Physiology at IMES. “This updated database is a vital resource for education, including in many courses at institutions like Harvard, MIT and Stanford; and training, as well as low-resource institutions,” said Jesse D. Raffa, research scientist in the Lab for Computational Physiology at IMES.
The eICU-CRD is the only dataset containing detailed critical care data from over 200 hospitals across the U.S., representing many ‘real-world’ challenges for successful deployment of algorithms and models, which are often not readily apparent in single-center datasets. Unlike other organizations that do not share data or only share single source data sets, Philips shares its data with credentialled researchers to help advance AI for improving outcomes in human health. More than 3,000 users have used the original database with citations in over 660 published academic research papers, including in Nature, The New England Journal of Medicine and the Journal of the American Medical Association.
“This initiative demonstrates our commitment to advancing machine learning and AI efforts, by making eICU data available for global research initiatives,” said Shiv Gopalkrishnan, General Manager of EMR & Care Management at Philips. “This is how we can enhance patient care and improve clinical outcomes: liberating and connecting data across systems and applications with integrated devices, systems and informatics, which can inform research with patient insights that can help clinicians make the right decision at the right time for their patients.”