关于自动化临床抑郁症诊断的系统审查
Kaining Mao1, Yuqi Wu1, Jie Chen2
1Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
Npj mental health research
|April 12, 2024
概括
分析语音,文本和面部表情的机器学习模型对检测抑郁症有很大的希望. 需要进一步的研究来开发用于临床心理健康评估的透明,多式联络机器学习.
科学领域:
- 计算精神病学是一种计算精神病学.
- 数字心理健康数字心理健康
- 机器学习在医疗保健中的应用
背景情况:
- 评估和治疗心理健康障碍是具有挑战性的,因为有限的护理和不可预测的症状模式.
- 机器学习 (ML) 通过分析临床数据提供了改善诊断和治疗的潜力.
- 语音,文本和面部表情分析是用于抑郁症检测的新兴ML技术.
研究的目的:
- 系统地审查过去十年的研究,使用语音,文字和面部表情分析来检测抑郁症.
- 在抑郁症检测研究中总结ML技术,数据集和临床结果评估.
- 确定挑战,并为开发临床适用的精神健康ML模型提供指导方针.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 对264项研究 (从最初的544项) 的分析,重点关注ML用于抑郁症检测.
- 关于参与者数量,结果措施,ML算法和特征提取技术的数据提取.
主要成果:
- 创建了一个数据库,总结了ML模型中用于抑郁症检测的特征.
- 确定了ML在提高心理健康评估方面的潜力.
- 强调需要更透明和可通用的ML模型用于临床使用.
结论:
- ML显示了改善心理健康障碍评估和治疗的巨大潜力.
- 克服模型透明度和通用性的需求等挑战对于临床采用至关重要.
- 为数据收集和ML模型培训提出了指导方针,以确保可重现性和跨语境适用性.
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