Decisions, Decisions: Machine Learning as a Tool to Identify Alcohol-use Disorder Treatment Seekers

Abstract

Alcohol use disorders (AUD) are very common in the developed world [1], yet only a minority of individuals with AUD seek treatment. Several factors influence the choice to seek treatment, including demographic, psychological and physical impediments. Integrating information from a number of disparate data sources is challenging. In this issue of EClinicalMedicine, Lee et al. [2] report a machine learning analysis that classified individuals with AUD as either treatment seekers or non-seekers. Notable strengths of this study included the examination of a wide range of predictor variables, the application of an innovative data analysis method (alternating decision trees; ADTs), and the use of an external validation sample to quantify reproducibility. There are, however, caveats that apply to the use of machine-learning methods in biomedical research.