Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.

Journal: Annals of emergency medicine
Published Date: May 8, 2021

Abstract

STUDY OBJECTIVE: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic health records to predict the probability of patient admission to either an inpatient unit or ICU at each hour (up to 24 hours) of an emergency department (ED) encounter. The secondary goal was to provide a framework for the operational implementation of these machine learning models.

Authors

  • Alexander Fenn
    Duke University School of Medicine, Durham, NC; Duke Institute of Health Innovation, Durham, NC. Electronic address: alexanderfenn@gmail.com.
  • Connor Davis
    Duke Institute of Health Innovation, Durham, NC.
  • Daniel M Buckland
    Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Durham, NC.
  • Neel Kapadia
    Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Durham, NC.
  • Marshall Nichols
    Duke Institute for Health Innovation, Durham, North Carolina.
  • Michael Gao
    Duke Institute for Health Innovation.
  • William Knechtle
    Duke Institute of Health Innovation, Durham, NC.
  • Suresh Balu
    Duke Institute for Health Innovation.
  • Mark Sendak
    Duke Institute for Health Innovation.
  • B Jason Theiling
    Division of Emergency Medicine, Department of Surgery, Duke University School of Medicine, Durham, NC.

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