Applications of machine learning to undifferentiated chest pain in the emergency department: A systematic review.

Journal: PloS one
PMID: 34428208

Abstract

BACKGROUND: Chest pain is amongst the most common reason for presentation to the emergency department (ED). There are many causes of chest pain, and it is important for the emergency physician to quickly and accurately diagnose life threatening causes such as acute myocardial infarction (AMI). Multiple clinical decision tools have been developed to assist clinicians in risk stratifying patients with chest. There is growing recognition that machine learning (ML) will have a significant impact on the practice of medicine in the near future and may assist with diagnosis and risk stratification. This systematic review aims to evaluate how ML has been applied to adults presenting to the ED with undifferentiated chest pain and assess if ML models show improved performance when compared to physicians or current risk stratification techniques.

Authors

  • Jonathon Stewart
    Royal Perth Hospital, Perth, Western Australia, Australia.
  • Juan Lu
    Yunnan Agricultural University, Kunming, China.
  • Adrian Goudie
    Department of Emergency Medicine, Fiona Stanley Hospital, Murdoch, Western Australia, Australia.
  • Mohammed Bennamoun
    School of Physics, Mathematics and Computing, University of Western Australia, Australia.
  • Peter Sprivulis
    Royal Perth Hospital, Perth, Western Australia, Australia.
  • Frank Sanfillipo
    School of Population and Global Health, University of Western Australia, Crawley, Western Australia, Australia.
  • Girish Dwivedi
    Department of Medicine, The University of Western Australia, 35 Stirling Highway, CRAWLEY Western Australia 6009, Australia.

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