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Mental health disorders remain a significant challenge in modern healthcare,with diagnosis and treatment often relying on subjective patient descriptionsand past medical history. To address this issue, we propose a personalizedmental health tracking and mood prediction system that utilizes patientphysiological data collected through personal health devices. Our systemleverages a decentralized learning mechanism that combines transfer andfederated machine learning concepts using smart contracts, allowing data toremain on users' devices and enabling effective tracking of mental healthconditions for psychiatric treatment and management in a privacy-aware andaccountable manner. We evaluate our model using a popular mental health datasetthat demonstrates promising results. By utilizing connected health systems andmachine learning models, our approach offers a novel solution to the challengeof providing psychiatrists with further insight into their patients' mentalhealth outside of traditional office visits.

MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment