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Peer-Reviewed Publication
Chest2024;165(5):1139-1148.May 1, 2024Journal Article

Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults.

Craig M Lilly1, David Kirk2, Itai M Pessach3, Gurudev Lotun4, Ofer Chen5, Ari Lipsky6, Iris Lieder5, Gershon Celniker5, Eric W Cucchi4, James M Blum7
1Department of Medicine, UMass Memorial Medical Center, Worcester, MA; UMass Memorial Health, UMass Memorial Medical Center, Worcester, MA; Department of Anesthesiology and Surgery, University of Massachusetts, Worcester, MA; University of Massachusetts Chan Medical School, University of Massachusetts, Worcester, MA; Clinical and Population Health Research Program, University of Massachusetts, Worcester, MA; Graduate School of Biomedical Sciences, University of Massachusetts, Worcester, MA. Electronic address: craig.lilly@umassmed.edu.
2WakeMed Health & Hospitals, Raleigh/Cary, NC.
3The Chaim Sheba Medical Center and Tel-Aviv University, Tel Hashomer, Israel; Clew Medical, Netanya, Israel.
4UMass Memorial Health, UMass Memorial Medical Center, Worcester, MA.
5Clew Medical, Netanya, Israel.
6The Chaim Sheba Medical Center and Tel-Aviv University, Tel Hashomer, Israel; Department of Emergency Medicine, Rambam Health Care Campus, Haifa, Israel.
7Department of Anesthesiology, University of Iowa, Iowa City, IA.
PubMed PMC DOI PDF

Abstract

BACKGROUND: Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur. RESEARCH QUESTION: Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of…

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