Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine.

Journal: Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Published Date: Mar 15, 2019

Abstract

OBJECTIVE: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs).

Authors

  • Paul Arora
    Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Lighthouse Outcomes, Toronto, ON, Canada. Electronic address: paul.arora@utoronto.ca.
  • Devon Boyne
    Lighthouse Outcomes, Toronto, ON, Canada; University of Calgary Cumming School of Medicine, Calgary, AB, Canada.
  • Justin J Slater
    Lighthouse Outcomes, Toronto, ON, Canada.
  • Alind Gupta
    Lighthouse Outcomes, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.
  • Darren R Brenner
    Department of Oncology and Community Health Sciences, University of Calgary Cumming School of Medicine, Calgary, AB, Canada.
  • Marek J Druzdzel
    School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.

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