调整大型语言模型以通过症状划分和总结来增强精神病学采访:试点研究
Jae-Hee So1, Joonhwan Chang1, Eunji Kim2,3
1Department of Applied Statistics, Yonsei University, Seoul, Republic of Korea.
JMIR formative research
|October 24, 2024
概括
大型语言模型 (LLM) 可以准确地识别精神病症状,并从面试成绩单中总结患者的压力因素. 这项技术显示出在临床环境中协助心理健康专业人员的前景.
科学领域:
- 人工智能在心理健康中的作用
- 精神病学中的自然语言处理.
- 计算精神病学是一种计算精神病学.
背景情况:
- 大型语言模型 (LLM) 在许多领域都在迅速发展.
- 精神病学面试虽然有结构,但仍然是LLM申请的探索不足的领域.
- 这项研究分析了来自朝鲜叛逃者受创伤和心理健康问题的咨询数据.
研究的目的:
- 确定LLM是否可以在对话记录中识别精神病症状及其指标.
- 评估LLM能力,从面试对话中总结患者的压力因素和症状.
主要方法:
- 法律学士的任务是提取压力因素,用指示性转录部分划分症状,并总结患者信息.
- 心理健康专家为培训和评估LLM绩效提供了标记数据.
- 使用GPT-4 Turbo和微调方法进行了零射击推断.
主要成果:
- 通过微调,LLMs在症状划分方面实现了超过0.8的准确性,优于零射击GPT-4 Turbo.
- 由LLM生成的摘要通过G-Eval.显示了高连贯性 (4.66) 和相关性 (4.67) 通过G-Eval.
- 在这种情况下,检索增强生成并没有显著提高LLM的性能.
结论:
- 通过专家标记的数据和适当的提示,LLM可以在精神病症状划分方面实现高准确性.
- 通过加强精神病学面试,LLM在协助心理健康从业者方面显示出显著的潜力.
- 这项研究强调了LLM在分析复杂的心理健康数据方面的有效性.
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