Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration.

Journal: Critical care explorations
PMID: 39258951

Abstract

BACKGROUND: Prediction-based strategies for physiologic deterioration offer the potential for earlier clinical interventions that improve patient outcomes. Current strategies are limited because they operate on inconsistent definitions of deterioration, attempt to dichotomize a dynamic and progressive phenomenon, and offer poor performance.

Authors

  • Supreeth P Shashikumar
  • Joshua Pei Le
    School of Medicine, University of Limerick, Limerick, Ireland.
  • Nathan Yung
    Division of Hospital Medicine, University of California San Diego, San Diego, CA.
  • James Ford
    Department of Emergency Medicine, University of California San Francisco, San Francisco, CA.
  • Karandeep Singh
    Department of Internal Medicine and School of Information, University of Michigan, Ann Arbor, Michigan.
  • Atul Malhotra
    Division of Pulmonary, Critical Care, and Sleep Medicine, University of California, San Diego, La Jolla, CA. Electronic address: amalhotra@health.ucsd.edu.
  • Shamim Nemati
    Department of Biomedical Informatics, Emory School of Medicine, Atlanta, GA, USA.
  • Gabriel Wardi
    Emergency Medicine, University of California San Diego, La Jolla, California, USA.

Keywords

External Resources

Popular Topics
Recent Journals