Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38587875

Abstract

OBJECTIVE: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data.

Authors

  • Jifan Gao
    Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison.
  • Guanhua Chen
    Vanderbilt University School of Medicine, Nashville, TN.
  • Ann P O'Rourke
    Department of Surgery, University of Wisconsin, 600 Highland Avenue, MC 3236, Madison, WI, United States.
  • John Caskey
    Department of Medicine, University of Wisconsin, Madison, USA.
  • Kyle A Carey
    Department of Medicine, University of Chicago, Chicago IL, United States.
  • Madeline Oguss
    Department of Medicine, University of Wisconsin, Madison, USA.
  • Anne Stey
    Division of Trauma and Surgical Critical Care, Department of Surgery, Northwestern University, 76 North St. Clair Street, Suite 650, Chicago, IL, United States.
  • Dmitriy Dligach
    Department of Public Health Sciences, Stritch School of Medicine, Loyola University Chicago, Maywood, IL.
  • Timothy Miller
    School of Computing and Information Systems, University of Melbourne, Victoria 3010, Australia.
  • Anoop Mayampurath
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Matthew M Churpek
    Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
  • Majid Afshar
    Loyola University Chicago, Chicago, IL.

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