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Automated Identification Of Unhealthy Drinking Using Routinely Collected Data: A Machine Learning Approach, Levi N. Bonnell, Benjamin Littenberg, Safwan R. Wshah, Gail L. Rose

Larner College of Medicine Faculty Publications

Background: Unhealthy drinking is prevalent in the United States and can lead to serious health and social consequences, yet it is under-diagnosed and under-treated. Identifying unhealthy drinkers can be time-consuming for primary care providers. An automated tool for identification would allow attention to be focused on patients most likely to need care and therefore increase efficiency and effectiveness.

Objectives: To build a clinical prediction tool for unhealthy drinking based solely on routinely collected demographic and laboratory data.

Methods: We obtained demographic and laboratory data on 89,325 adults seen at the University of Vermont Medical Center from 2011-2017. Logistic regression, …