Machine Learning-Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review.

Journal: Journal of medical Internet research
Published Date: Feb 4, 2021

Abstract

BACKGROUND: Timely identification of patients at a high risk of clinical deterioration is key to prioritizing care, allocating resources effectively, and preventing adverse outcomes. Vital signs-based, aggregate-weighted early warning systems are commonly used to predict the risk of outcomes related to cardiorespiratory instability and sepsis, which are strong predictors of poor outcomes and mortality. Machine learning models, which can incorporate trends and capture relationships among parameters that aggregate-weighted models cannot, have recently been showing promising results.

Authors

  • Sankavi Muralitharan
    Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.
  • Walter Nelson
    Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada.
  • Shuang Di
    Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, Canada.
  • Michael McGillion
    School of Nursing, McMaster University, Hamilton, ON, Canada.
  • P J Devereaux
    Population Health Research Institute, Hamilton, ON, Canada.
  • Neil Grant Barr
    Health Policy and Management, DeGroote School of Business, McMaster University, Hamilton, ON, Canada.
  • Jeremy Petch
    Population Health Research Institute, McMaster University and Hamilton Health Sciences, Hamilton, Canada.

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