Machine Learning Models for Personalized Medication Adherence Interventions in Chronic Disease Management

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Takashi Mori

Independent Researcher

Chuo-ku, Sapporo, Japan (JP) – 060-0042

Abstract

Medication nonadherence is a persistent barrier to effective chronic disease management, leading to preventable morbidity, hospitalizations, and costs. Traditional, one-size-fits-all adherence programs (e.g., reminder calls, generic SMS messages) rarely account for the heterogeneous and dynamic drivers of nonadherence—such as regimen complexity, side effects, beliefs, behavioral routines, and social determinants of health. This manuscript proposes and details a pragmatic, privacy-preserving study that leverages machine learning (ML) to (i) predict near-term nonadherence risk, (ii) estimate heterogeneous treatment effects to select the right intervention for the right patient, and (iii) adaptively personalize intensity and modality of support over time. We outline an end-to-end pipeline combining structured electronic health records, pharmacy dispensing data, patient-reported outcomes, and passively collected digital traces. The core modeling toolkit integrates gradient-boosted trees for short-horizon risk prediction, uplift models and causal forests for treatment selection, and contextual bandits to adapt intervention policies during deployment.

A prospective, multi-site, 6-month randomized adaptive trial is described with proportion of days covered (PDC) as the primary endpoint, complemented by clinical and patient-centered outcomes. A simulated evaluation on de-identified historical data (N≈10,000) suggests that personalized allocation improves PDC by 8–12 percentage points over standard outreach, with the largest gains among polypharmacy and newly initiated patients. Fairness-aware training and interpretable explanations (SHAP) are used to support equitable, clinician-facing decisions. If validated prospectively, this framework can transform adherence support from generic reminders into data-driven, individualized behavioral care embedded in routine practice.

Key words

medication adherence; chronic disease; machine learning; personalization; uplift modeling; contextual bandits; digital health; fairness

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