Personalized Medication Adherence Prediction using Machine Learning Models: A Case Study in Chronic Disease Management

Abstract

This research paper presents a data-driven approach to predict medication adherence behavior among patients with chronic diseases using machine learning models. By integrating diverse patient data sources, including electronic health records, wearable device data, and socio-demographic information, our predictive models can identify factors influencing medication adherence and forecast patient-specific adherence patterns over time. Implementation of these models in clinical practice can facilitate targeted interventions, improve medication adherence rates, and enhance overall patient care in chronic disease management.