Machine learning models for predicting unscheduled return visits to an emergency department: a scoping review.

Journal: BMC emergency medicine
Published Date: Jan 30, 2024

Abstract

BACKGROUND: Unscheduled return visits (URVs) to emergency departments (EDs) are used to assess the quality of care in EDs. Machine learning (ML) models can incorporate a wide range of complex predictors to identify high-risk patients and reduce errors to save time and cost. However, the accuracy and practicality of such models are questionable. This review compares the predictive power of multiple ML models and examines the effects of multiple research factors on these models' performance in predicting URVs to EDs.

Authors

  • Yi-Chih Lee
    Department of International Business, Chien Hsin University of Science and Technology, Taoyuan, Taiwan.
  • Chip-Jin Ng
    Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan.
  • Chun-Chuan Hsu
    Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan.
  • Chien-Wei Cheng
    Department of Emergency Medicine, Chang Gung Memorial Hospital, Keelung and Chang Gung University, College of Medicine, No. 5 Fushing St., Gueishan Shiang, Taoyuan City, 333, Taiwan.
  • Shou-Yen Chen
    Department of Emergency Medicine, Chang Gung Memorial Hospital and Chang Gung University, College of Medicine, Taoyuan City, 333, Taiwan. allendream0621@yahoo.com.tw.

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