A Machine Learning Approach to Identification of Unhealthy Drinking.

Journal: Journal of the American Board of Family Medicine : JABFM
Published Date: Jan 1, 2020

Abstract

INTRODUCTION: Unhealthy drinking is prevalent in the United States, and yet it is underidentified and undertreated. Identifying unhealthy drinkers can be time-consuming and uncomfortable for primary care providers. An automated rule for identification would focus attention on patients most likely to need care and, therefore, increase efficiency and effectiveness. The objective of this study was to build a clinical prediction tool for unhealthy drinking based on routinely available demographic and laboratory data.

Authors

  • Levi N Bonnell
    From University of Vermont College of Medicine, Burlington (LNB, BL, GLR); University of Vermont, College of Engineering and Mathematical Sciences, Burlington (SRW). levi.bonnell@med.uvm.edu.
  • Benjamin Littenberg
    From University of Vermont College of Medicine, Burlington (LNB, BL, GLR); University of Vermont, College of Engineering and Mathematical Sciences, Burlington (SRW).
  • Safwan R Wshah
    From University of Vermont College of Medicine, Burlington (LNB, BL, GLR); University of Vermont, College of Engineering and Mathematical Sciences, Burlington (SRW).
  • Gail L Rose
    From University of Vermont College of Medicine, Burlington (LNB, BL, GLR); University of Vermont, College of Engineering and Mathematical Sciences, Burlington (SRW).

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