A Review of Machine Learning Approaches for Predicting Depression Readmission Risk and the Role of Mobile Health Technology in Early Intervention

Depression poses a significant global health burden, largely due to high rates of relapse and hospital readmission following initial treatment. This review synthesizes current research on leveraging machine learning (ML) models to predict depression readmission risk, exploring key predictive features derived from behavioural, physiological, and digital sources. It examines the integration of ML with mobile health (mHealth) technologies to facilitate real-time monitoring and early intervention. The review also addresses crucial ethical considerations, including data privacy, algorithmic bias, and fairness in deploying AI-driven mental health solutions. Emphasis is placed on the need for explainable and equitable AI, robust data governance, and the value of open, de-identified datasets. Finally, it identifies challenges and opportunities for future deployment in low- and middle-income countries (LMICs), with a focus on digital equity and culturally relevant applications.