数据分析为心理健康的数字表型:一个系统的文献综述
Wesllei Felipe Heckler1, Luan Paris Feijó2, Juliano Varella de Carvalho3
1Applied Computing Graduate Program (PPGCA), University of Vale do Rio dos Sinos, Unisinos Avenue, 950, Cristo Rei, São Leopoldo, Rio Grande do Sul, 93022-750, Brazil.
Artificial intelligence in medicine
|March 9, 2025
概括
数字表型化使用智能手机数据来检测心理健康症状. 本次审查强调了日益增长的研究兴趣和需要被动数据收集以帮助治疗精神障碍.
科学领域:
- 计算机科学 计算机科学
- 数字健康数字健康
- 心理健康研究 心理健康研究
背景情况:
- 精神障碍影响全球数百万人,未经治疗的疾病导致显著的死亡率.
- 智能手机和可穿戴设备的普及使人类表型的持续,现场测量成为可能.
- 数字表型利用个人设备数据来识别潜在的心理健康症状.
研究的目的:
- 为心理健康中的数字表型化提供数据分析的计算机科学视角.
- 系统地审查关于心理健康数字表型化的现有文献.
- 确定这一新兴研究领域的趋势,挑战和机遇.
主要方法:
- 从10个学术数据库中对5,422篇文章进行系统文献审查,截至2024年9月.
- 分析重点是数据类型,使用的设备和数据分析技术.
- 调查了十个研究问题,涵盖应用领域,心理健康状况和分析策略.
主要成果:
- 观察到,对心理健康的数字表型化研究的研究显著增加.
- 目前的方法通常依赖于自我报告的数据,但通过智能手机被动数据收集正在获得引力.
- 传统的机器学习技术主导着数据分析,重点关注分析方法.
结论:
- 数字表型化对心理健康监测和干预具有前景.
- 需要超越自我报告的数据,转向更客观,被动收集的数字生物标志物.
- 进一步的研究可以优化数据分析,为患有精神障碍的人士开发有效的工具.
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