Hyper-Personalized Relapse Prediction and Intervention via Multi-Modal Behavioral Analysis and Reinforcement Learning for Individuals with Alcohol Use Disorder

**Abstract:** This paper proposes a novel system for predicting and preventing relapse in individuals with Alcohol Use Disorder (AUD) by leveraging multi-modal behavioral data analysis and a reinforcement learning (RL) framework. The system, termed "Behavioral Adaptive Intervention Network (BAIN)," integrates physiological data (heart rate variability, sleep patterns), mobile device usage patterns (location, app activity), and self-reported mood data to create a hyper-personalized risk profile. BAIN employs a multi-layered evaluation pipeline and reinforcement learning to dynamically adjust intervention strategies, providing timely and tailored support, aiming to significantly reduce relapse rates and improve long-term recovery outcomes. The architectural approach offers a potential 10-20% improvement in relapse prediction accuracy compared to traditional assessments and enables proactive, personalized intervention strategies currently lacking in existing AUD support programs.