对预测心理健康风险的联合和云计算方法进行系统审查
Iram Fiaz1, Nadia Kanwal1, Amro Al-Said Ahmad1
1School of Computer Science and Mathematics, Keele University, Newcastle ST5 5BG, UK.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
联合学习 (FL) 能够对跨设备的敏感数字心理健康数据进行隐私保护分析. 虽然有希望,但进展是分散的,需要更多的工作在现实世界的部署和道德标准.
科学领域:
- 数字健康数字健康
- 心理健康技术 心理健康技术
- 机器学习 机器学习
背景情况:
- 在全球范围内,精神健康障碍是导致残疾的主要原因.
- 数字健康技术为个性化护理生成有价值的行为数据.
- 数据隐私和可扩展性是分析敏感心理健康数据的主要挑战.
研究的目的:
- 审查使用联合学习 (FL) 进行数字心理健康的进展和挑战.
- 确定在精神卫生保健中开发强大的FL系统的关键领域.
主要方法:
- 对1104项记录进行系统审查,根据质量选择了17项经验研究.
- 分析了精选的研究,重点是FL和边缘/云架构,数据源,隐私技术和现实应用.
- 基于研究方法和发现的研究比较.
主要成果:
- 联合学习与边缘/雾/云计算相结合,显示了实时心理健康分析的潜力.
- 在FL的数字心理健康方面的进展是创新的,但分散的.
- 关于并发症建模,部署评估和标准化基准的研究有限.
结论:
- 联合学习为分析数字心理健康数据提供了一种保护隐私的方法.
- 对于可扩展和道德FL系统的部署,评估和标准化等领域需要进一步发展.
- 优先事项包括解决并发症,现实世界的验证,并建立共同的基准.
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