心理健康咨询从对话内容与基于变压器的机器学习的对话内容
Zac E Imel1, Michael J Tanana1, Christina S Soma1
1Lyssn.io, Seattle, Washington.
JAMA network open
|January 22, 2024
概括
机器学习分析了基于文本的咨询,发现了支持性干预措施,改善了客户的结果和满意度. 这种可扩展的方法通过评估临床内容和参与度来增强心理治疗研究.
科学领域:
- 数字健康数字健康
- 医疗保健中的机器学习
- 行为健康 行为健康
背景情况:
- 异步基于文本的辅导是一个不断增长的,可访问的行为健康护理模式.
- 评估基于文本的治疗中的过程和内容在规模上仍然是一个挑战.
研究的目的:
- 采用机器学习 (ML) 来评估临床内容和客户报告的结果,在大量的基于文本的咨询会议中.
- 在数字心理健康中识别治疗干预,内容和客户结果之间的关联.
主要方法:
- 一项质量改进研究分析了2014-2019年期间通过移动应用程序提供的异步基于文本的辅导的无身份文本信息.
- 一个基于变压器的深度学习模型自动分类消息内容和干预.
- 混合效应回归评估了干预,内容和客户结果 (参与,满意度,PHQ-8症状变化) 之间的关联.
主要成果:
- 该研究包括166,644名客户和20,600,274条消息,参与者主要是女性,年龄26-35岁,白人.
- ML分析显示了治疗师干预和内容的显著变化.
- 治疗干预和内容与客户参与 (R=0.43),满意度 (R=0.46) 和症状改善 (PHQ-8变化,R=0.13) 相关.
结论:
- 辅导辅导干预与改善客户结果有关,反映了传统治疗中的发现.
- 基于机器学习的内容评估为心理治疗研究提供了可扩展和特定的方法.
- 这种方法可以提高数字心理健康服务的理解和质量.
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