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

Journal: Chest
PMID: 37923292

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.

Authors

  • Craig M Lilly
    Department 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.
  • David Kirk
    WakeMed Health & Hospitals, Raleigh/Cary, NC.
  • Itai M Pessach
    The Chaim Sheba Medical Center and Tel-Aviv University, Tel Hashomer, Israel; Clew Medical, Netanya, Israel.
  • Gurudev Lotun
    UMass Memorial Health, UMass Memorial Medical Center, Worcester, MA.
  • Ofer Chen
    Clew Medical, Netanya, Israel.
  • Ari Lipsky
    The Chaim Sheba Medical Center and Tel-Aviv University, Tel Hashomer, Israel; Department of Emergency Medicine, Rambam Health Care Campus, Haifa, Israel.
  • Iris Lieder
    Clew Medical, Netanya, Israel.
  • Gershon Celniker
    Clew Medical, Netanya, Israel.
  • Eric W Cucchi
    UMass Memorial Health, UMass Memorial Medical Center, Worcester, MA.
  • James M Blum
    Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, United States of America.

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