Prediction of sepsis mortality in ICU patients using machine learning methods.

Journal: BMC medical informatics and decision making
Published Date: Aug 16, 2024

Abstract

PROBLEM: Sepsis, a life-threatening condition, accounts for the deaths of millions of people worldwide. Accurate prediction of sepsis outcomes is crucial for effective treatment and management. Previous studies have utilized machine learning for prognosis, but have limitations in feature sets and model interpretability.

Authors

  • Jiayi Gao
    Department of Industrial System Engineering, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
  • Yuying Lu
    Department of Industrial System Engineering, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
  • Negin Ashrafi
    Department of Industrial and Systems Engineering, University of Southern California (USC), Los Angeles, CA, United States of America.
  • Ian Domingo
    Department of Information and Computer Science, University of California, Irvine, Inner Ring Rd, Irvine, CA, 92697, USA.
  • Kamiar Alaei
    Department of Health Science, California State University, Long Beach, 1250 Bellflower Blvd. HHS2-117, Long Beach, CA, 90840, USA.
  • Maryam Pishgar
    Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60609, USA.

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