Applying machine learning to predict real-world individual treatment effects: insights from a virtual patient cohort.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Oct 1, 2019

Abstract

OBJECTIVE: We aimed to investigate bias in applying machine learning to predict real-world individual treatment effects.

Authors

  • Gang Fang
    Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut, USA.
  • Izabela E Annis
    Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Jennifer Elston-Lafata
    Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
  • Samuel Cykert
    Program for Health and Clinical Informatics, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

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