aceAn unsupervised machine learning analysis of biopsychosocial characteristics and treatment outcome of alcohol use disorder
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 st…
K-means clustering of eight biopsychosocial variables identified three distinct AUD profiles that predicted 3-month abstinence, with Late-Onset achieving 65.8% abstinence, supporting stratified front-loaded intervention.
Patients classified as Severe profile with early onset, 73.3% co-occurring psychiatric conditions and intense craving had poorer 3-month outcomes with only 36.7% abstinence and steep return to use.
Single-center retrospective design with 102 patients at a tertiary care center in India limits generalizability and causal inference for broader AUD populations.
Evidence
- Peer-reviewedJournal of Addictive Diseases2026-08-07
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Truvace Impact Record TRV-2026-0687, v1: “An unsupervised machine learning analysis of biopsychosocial characteristics and treatment outcome of alcohol use disorder.” Truvace, 2026-08-08. /record/TRV-2026-0687 (accessed at citation time). sha256 0d137fa2fdffe185…
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