Adaptive Medication Adherence Optimization Using Behavioral Analytics and Multi-Model AI Prediction

Abstract

Medication non-adherence remains a major contributor to preventable disease progression and increased healthcare utilization. Traditional reminder systems follow static schedules and fail to adapt to individual behavior, often resulting in notification fatigue and poor long-term engagement. This work presents an adaptive adherence optimization framework that integrates behavioral analytics and multi-model AI prediction to personalize intervention timing and frequency. The system combines demographic trends, engagement patterns, and risk signals derived from gradient-boosted trees, LSTM models, and transformer-based time-series predictors. A continuous feedback-learning mechanism further refines personalization based on user sentiment and interaction data. Real-world evaluation demonstrates improved adherence, higher notification engagement, and reduced alert fatigue, highlighting the value of adaptive, data-driven approaches for scalable digital therapeutics.

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