Advancing Emergency Department Triage Prediction With Machine Learning to Optimize Triage for Abdominal Pain Surgery Patients.

Journal: Surgical innovation
PMID: 39150388

Abstract

BACKGROUND: The development of emergency department (ED) triage systems remains challenging in accurately differentiating patients with acute abdominal pain (AAP) who are critical and urgent for surgery due to subjectivity and limitations. We use machine learning models to predict emergency surgical abdominal pain patients in triage, and then compare their performance with conventional Logistic regression models.

Authors

  • Chen Chai
    Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitation, Zhongnan Hospital of Wuhan University, Wuhan, China.
  • Shu-Zhen Peng
    Wuhan University School of Medicine, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
  • Rui Zhang
    Department of Cardiology, Zhongda Hospital, Medical School of Southeast University, Nanjing, China.
  • Cheng-Wei Li
    Information Center, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
  • Yan Zhao
    Emergency Center, Hubei Clinical Research Center for Emergency and Resuscitaion, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.

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