MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment

TL;DR

MentalHealthAI introduces a privacy-preserving, personalized mental health monitoring system that leverages decentralized machine learning on personal health device data to improve psychiatric treatment insights.

Contribution

The paper presents a novel decentralized learning framework combining transfer and federated learning for mental health tracking using personal device data.

Findings

Promising results on mental health dataset

Effective privacy-aware mental health monitoring

Enhanced psychiatric treatment insights

Abstract

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.