A machine learning-based prediction of hospital mortality in mechanically ventilated ICU patients.

Journal: PloS one
PMID: 39231126

Abstract

BACKGROUND: Mechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts.

Authors

  • Hexin Li
    State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin, China.
  • Negin Ashrafi
    Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles, CA, United States of America.
  • Chris Kang
    Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles, CA, United States of America.
  • Guanlan Zhao
    Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles, CA, United States of America.
  • Yubing Chen
    Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles, CA, United States of America.
  • Maryam Pishgar
    Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609, USA.

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