Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

Journal: The journal of trauma and acute care surgery
Published Date: Jan 1, 2022

Abstract

INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack of integration with real-time patient data. The electronic health record (EHR) has the ability to use machine learning (ML) to develop predictive models. While an EHR ML model has been developed to predict clinical deterioration, it has yet to be validated for use in trauma. We hypothesized that the Epic Deterioration Index (EDI) would predict mortality and unplanned intensive care unit (ICU) admission in trauma patients.

Authors

  • Zongyang Mou
    From the Department of Surgery, Division of Trauma, Surgical Critical Care, Burns and Acute Care Surgery (Z.M., L.N.G., A.E.B., J.J.D., T.W.C.), and Department of Medicine (R.E.-K.), University of California San Diego School of Medicine, San Diego, California.
  • Laura N Godat
  • Robert El-Kareh
    Division of Biomedical Informatics, UCSD, San Diego, CA, USA.
  • Allison E Berndtson
  • Jay J Doucet
  • Todd W Costantini

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