Predicting hospital admission for older emergency department patients: Insights from machine learning.

Journal: International journal of medical informatics
Published Date: Aug 1, 2020

Abstract

BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories of older adults seeking medical attention. Older adults present to the ED with distinct needs and complex medical histories, which can make disposition planning more challenging. Machine learning (ML) approaches have been previously used to inform decision-making surrounding ED disposition in the general population. However, little is known about the performance and utility of ML methods in predicting hospital admission among older ED patients. We applied a series of ML algorithms to predict ED admission in older adults and discuss their clinical and policy implications.

Authors

  • Fabrice Mowbray
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Big Data and Geriatric Models of Care (BDG) Cluster, McMaster University, Hamilton, Ontario, Canada.
  • Manaf Zargoush
    Health Policy and Management, DeGroote School of Business, McMaster University, Hamilton, Ontario, Canada. Electronic address: zargoush@mcmaster.ca.
  • Aaron Jones
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Big Data and Geriatric Models of Care (BDG) Cluster, McMaster University, Hamilton, Ontario, Canada.
  • Kerstin de Wit
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Division of Emergency Medicine, Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
  • Andrew Costa
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada; Big Data and Geriatric Models of Care (BDG) Cluster, McMaster University, Hamilton, Ontario, Canada.

Keywords

External Resources

Popular Topics
Recent Journals