使用大型语言模型来检测青少年抑郁症的定性研究结果
Alison W Xin1, Dylan M Nielson1, Karolin Rose Krause2
1Machine Learning Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 11, 2024
概括
大型语言模型 (LLM) 可以在青少年抑郁症采访中准确地检测微妙的心理治疗结果. 这种方法支持量化超出简单分类的重要患者体验.
科学领域:
- 自然语言处理自然语言处理.
- 临床心理学 临床心理学
- 医疗保健中的人工智能
背景情况:
- 心理治疗中的传统结果分类通常是二元的.
- 开发了一种新的,细粒度的编码框架,用于治疗结果.
- 在临床抑郁症研究中,定性分析为这一框架提供了信息.
研究的目的:
- 利用大型语言模型 (LLM) 来检测微妙的心理治疗结果.
- 评估LLM嵌入在细粒度治疗结果分类中的有效性.
- 证明LLMs在分析临床访谈成绩单中的有用性.
主要方法:
- 评估了五个开源的LLM来对结果进行分类.
- 对原始采访成绩单进行了分类实验.
- 在细分数据上重复实验 (对话轮,独白).
主要成果:
- 分类模型预测了31个结果和8个衍生标签在3个细分.
- 在ROC曲线下面积得分范围从0.6到1.0.0.
- 通过独白和轮回细分获得了更高的分数.
结论:
- 基于LLM的模型可以识别青少年特定的结果,如友和学术表现.
- 与社交媒体研究相比,临床数据的概括性得到了增强.
- 在心理治疗文本中细粒度的结果编码是可行的,并支持量化.
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