MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment
This work addresses the problem of limited insight into patients' mental health for psychiatrists, offering a privacy-aware solution, though it appears incremental in its technical approach.
The authors tackled the challenge of subjective mental health diagnosis by developing a personalized tracking system that uses physiological data from personal health devices, achieving promising results in mood prediction.
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.