Adherence Prediction

Adherence prediction focuses on forecasting whether individuals will consistently engage with treatments or interventions, a crucial factor impacting healthcare outcomes and program effectiveness. Current research employs diverse machine learning approaches, including sequential models like LSTMs and Bayesian methods, to predict adherence based on both static patient characteristics and dynamic behavioral data, often addressing challenges like data imbalance and privacy concerns. Accurate adherence prediction enables proactive interventions to improve treatment success rates across various applications, from managing chronic diseases like tuberculosis to optimizing the delivery of mental health services.