深入探索机器学习方法用于检测心理健康状况:系统的审查和分析
Md Jawadul Hasan1, Shadril Hassan Shifat1, Joy Matubber1
1ELITE Research Lab, Queens, NY, United States.
Frontiers in digital health
|January 19, 2026
概括
机器学习在检测心理健康状况方面表现有前途. 本次审查强调了它的有效性,但需要进一步的研究来改进算法并解决更好的心理健康支持的伦理问题.
科学领域:
- 数字健康数字健康
- 计算精神病学是一种计算精神病学.
- 医疗保健中的人工智能
背景情况:
- 全球精神健康问题的上升是一个重大的公共卫生挑战.
- 社会耻辱妨碍个人寻求帮助,需要创新的支持系统.
- 机器学习 (ML) 正在成为心理健康诊断和干预的关键技术.
研究的目的:
- 系统地审查和分类用于心理健康检测的ML技术.
- 检查使用ML预测心理健康状况的研究.
- 编制数据集并分析经常用于心理健康评估的ML算法.
主要方法:
- 从2015年1月到2024年12月,在主要数据库 (Springer,ScienceDirect,IEEE,PubMed) 进行了广泛的文献搜索.
- 根据标题和摘要,在初步选3320篇文章后,选择了35篇文章.
- 分析了利用在线社交网络数据 (14项研究) 和手动数据收集 (21项研究) 的研究,采用各种监督和无监督的ML技术.
主要成果:
- 机器学习在检测心理健康状况方面表现出有效性和效率.
- 研究表明方法各不相同,其中14种方法使用社交网络数据,21种方法使用手动数据收集.
- 复杂的深度学习架构通常比逻辑回归等简单模型的性能更高,表明可解释性和准确性之间的权衡.
结论:
- 机器学习在应对心理健康挑战方面具有重大潜力.
- 进一步的研究至关重要,重点是改进采样,算法改进,伦理数据处理,并纳入像图像处理这样的技术.
- 人工智能研究人员和心理健康专家之间的合作对于提高研究有效性和影响至关重要.
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