Philips, MIT IMES Build Enhanced Critical Care Data Set

Philips has expanded its initiative with the Institute for Medical Engineering and Science (IMES) at the Massachusetts Institute of Technology (MIT) to provide healthcare researchers with a new, critical data care set to advance machine learning and artificial intelligence (AI) in healthcare.
 
The updated eICU Collaborative Research Database (eICU-CRD) includes de-identified data of 200,000 critical care patients, including those impacted by COVID-19. During the pandemic, eICU and critical care saw a significant growth of patients and unique challenges in the way care was provided, causing Philips and IMES to expand the original data set, first released in 2016.
 
The new database has de-identified and detailed clinical information like vital signs, pharmacy and medication orders, laboratory results, diagnoses, and novel severity of illness scores. The dataset can offer insights on patient treatments, co-morbidities, readmissions, and outcomes.
 
Researchers at Philips and the IMES Laboratory of Computational Physiology will grant researchers around the world access to the data to help develop advanced algorithms and provide new insights on critical care. The Laboratory of Computational Physiology will continue to serve as the initiative’s academic research hub and will provide and maintain access, as well as help educate researchers about the database and offer a platform for collaboration. The database is available for medical research, to those who are credentialed, take human subjects training, and agree to a data use agreement.
 
“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,” Leo Anthony Celi, principal research scientist and clinical research director at the Laboratory of Computational Physiology at IMES, told the press.
 
“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,” added Jesse D. Raffa, research scientist in the Lab for Computational Physiology at IMES.
 
The eICU-CRD dataset contains detailed critical care data from more than 200 U.S. hospitals. Philips shares its data with credentialled researchers to help advance AI to improve health outcomes. Over 3,000 centers have used the original database, with citations in over 660 published academic research papers.
 
“This initiative demonstrates our commitment to advancing machine learning and AI efforts, by making eICU data available for global research initiatives,” said Shiv Gopalkrishnan, GM 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.”