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.

翻译：精神健康障碍仍是现代医疗中的重大挑战，其诊断与治疗常依赖患者主观描述及既往病史。为解决此问题，我们提出一套个性化精神健康追踪与情绪预测系统，通过个人健康设备采集的生理数据实现分析。该系统采用去中心化学习机制，结合迁移学习与联邦机器学习概念，并基于智能合约运作，使数据留存于用户设备，同时以隐私保护且可问责的方式，有效追踪精神健康状况以辅助精神科治疗与管理。我们使用公开的精神健康数据集对模型进行评估，展示了颇具前景的结果。通过整合健康系统与机器学习模型，我们的方法为精神科医生在传统门诊之外获取患者精神健康更深入洞察这一挑战，提供了创新解决方案。