机器学习在分析工作中的心理健康:一个范围审查
Pekka Varje1, Ari Väänänen1, Olli Haavisto1
1Finnish Institute of Occupational Health, P.O. Box 40, FI-00032 Työterveyslaitos, Helsinki, Finland.
Journal of occupational health
|March 8, 2026
概括
机器学习在工作场所心理健康研究中表现有前途,但目前的研究主要集中在方法论上. 需要更多的跨学科合作来推进职业健康洞察力.
科学领域:
- 职业健康 职业健康 职业健康
- 计算机科学 计算机科学
- 精神病学是一个精神病学.
背景情况:
- 职场心理健康是一个日益关注的问题.
- 机器学习 (ML) 为分析复杂的心理健康数据提供了潜力.
- 现有的研究还没有系统地审查ML在这个领域的应用.
研究的目的:
- 在职场心理健康研究中对机器学习进行范围审查.
- 识别当前的方法,应用和趋势.
- 了解ML在职业心理健康中的当前状态和未来方向.
主要方法:
- 在多个数据库 (Ebsco,Scopus,Web of Science,PsycINFO,IEEE,ACM) 中进行全面搜索.
- 选了5600篇摘要,分析了92篇相关出版物.
- 包括2025年9月之前发表的期刊文章,会议论文和书籍章节.
主要成果:
- 自2020年以来,出版物的数量大幅增加.
- 横截面设计 (73%) 和监督学习方法 (例如,随机森林,神经网络) 的优势.
- 专注于抑郁症,倦怠和焦虑,纵向或流行病学研究有限.
结论:
- 在职业心理健康领域的ML应用还处于新生阶段,有着强烈的方法论重点.
- 数据科学家和健康/社会科学研究人员之间需要进行跨学科的合作.
- 需要进一步探索ML促进职业健康研究的潜力.
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