Title

Automated Identification of Unhealthy Drinking Using Routinely Collected Data: A Machine Learning Approach

Authors

Levi N. Bonnell, University of VermontFollow
Benjamin Littenberg, University of VermontFollow
Safwan R. Wshah, University of VermontFollow
Gail L. Rose, University of VermontFollow

Document Type

Poster

Publication Date

2019

Abstract

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, support vector machines (SVM), k-nearest neighbor, and random forests were each used to build clinical prediction models. The model with the largest area under the receiver operator curve (AUC) was selected.

Results: SVM with polynomials of degree 3 produced the largest AUC. The most influential predictors were alkaline phosphatase, gender, glucose, and serum bicarbonate. The optimum operating point had sensitivity 31.1%, specificity 91.2%, positive predictive value 50.4%, and negative predictive value 82.1%. Application of the tool increased the prevalence of unhealthy drinking from 18.3% to 32.4%, while reducing the target population by 22%.

Limitations: Universal screening was not used during the time data was collected. The prevalence of unhealthy drinking among those screened was 60% suggesting the AUDIT-C was administered to confirm rather than screen for unhealthy drinking.

Conclusion: An automated tool, using commonly available data, can identify a subset of patients who appear to warrant clinical attention for unhealthy drinking.

Recommended Citation

Bonnell, Levi N.; Littenberg, Benjamin; Wshah, Safwan R.; and Rose, Gail L., "Automated Identification of Unhealthy Drinking Using Routinely Collected Data: A Machine Learning Approach" (2019). Larner College of Medicine Faculty Publications. 9.
https://scholarworks.uvm.edu/comfac/9