An unsupervised machine learning analysis of biopsychosocial characteristics and treatment outcome of alcohol use disorder.

Abstract

Background: Alcohol Use Disorder is a heterogeneous condition where standard severity measures often fail to predict individual treatment responses. Precision medicine requires identifying distinct biopsychosocial profiles to guide targeted interventions.Objectives: To identify clinically meaningful Alcohol Use Disorder profiles using k-means clustering based on eight baseline biopsychosocial variables and validate their prognostic utility by comparing treatment outcomes.Methods: A retrospective observational study included 102 patients at a tertiary care center in India. K-means clustering was applied to baseline variables: age, Alcohol use duration, alcohol severity, craving, co-occurring psychiatric conditions, self-efficacy, education, and occupation. Cluster stability was assessed via bootstrap resampling. The primary outcome was 3-month abstinence, corroborated by GGT levels.Results: Three distinct profiles emerged: (1) Late-Onset (n = 38), with older age and lowest craving, achieving 65.8% abstinence; (2) High-Functioning (n = 34), defined by high socioeconomic status and zero co-occurring disorders; and (3) Severe (n = 30), characterized by early onset, high co-occurring psychiatric conditions (73.3%), and intense craving. Despite similar alcohol severity scores (p = 0.215), the Severe profile group had poorer outcomes (36.7% abstinence; p = 0.004) and a steep return to use trajectory.Conclusion: Identifying three distinct Alcohol Use Disorder profiles through k-means cluster analysis advances precision medicine in substance use treatment. Return to use risk was driven by craving and co-occurring conditions rather than AUD severity. These findings support a stratified approach, where Severe profile group patients require immediate, intensive "front-loaded" intervention.