Predicting population health with machine learning: a scoping review.

Journal: BMJ open
Published Date: Oct 27, 2020

Abstract

OBJECTIVE: To determine how machine learning has been applied to prediction applications in population health contexts. Specifically, to describe which outcomes have been studied, the data sources most widely used and whether reporting of machine learning predictive models aligns with established reporting guidelines.

Authors

  • Jason Denzil Morgenstern
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
  • Emmalin Buajitti
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Meghan O'Neill
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Thomas Piggott
    Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.
  • Vivek Goel
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Daniel Fridman
    Hospital for Sick Children, Toronto, Ontario, Canada.
  • Kathy Kornas
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
  • Laura C Rosella
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada laura.rosella@utoronto.ca.

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