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