用克洛扎治疗精神分裂症患者精神病症状的量化改善:用大型语言模型进行临床笔记分析
Misa Matsumura1, Keiichiro Nishida2, Katsunori Toyoda2
1Education and Research Center for Clinical Pharmacy, Faculty of Pharmacy, Osaka Medical and Pharmaceutical University, Takatsuki, Osaka, Japan.
Scientific reports
|February 12, 2026
概括
大型语言模型 (LLM) 可以分析临床笔记,跟踪治疗期间精神分裂症症状的变化. 这种自然语言处理方法提供了一种低负担的方法,可以使用电子健康记录来评估治疗的有效性.
科学领域:
- 精神病学和计算语言学
- 人工智能在医疗保健中的应用
背景情况:
- 精神分裂症的症状通常出现在患者的言语模式中.
- 自然语言处理 (NLP) 提供了对精神分裂症语言缺陷的定量评估.
- 现有的NLP研究往往忽视了与治疗相关的改善,而是专注于急性阶段或精神病预测.
研究的目的:
- 调查大语言模型 (LLM) 在评估从非结构化的电子健康记录 (EHR) 来评估精神分裂症症状的实用性.
- 为了评估与治疗相关的精神分裂症症状的变化,使用LLM分析在克洛扎宾治疗期间的临床笔记.
主要方法:
- 分析了来自30名接受克洛扎治疗的耐治疗精神分裂症患者的5275份临床笔记.
- 应用三种最先进的LLM来根据简要精神病学评分表 (BPRS) 进行症状评分.
- 补充语言分析,包括部分语音 (POS),词袋 (BoW) 和语言调查和词数 (LIWC).
主要成果:
- 基于LLM的BPRS评级显示,焦虑,概念失调,怀疑,不寻常的思想内容,幻觉行为和抑郁情绪的症状显著减少.
- 语音部分分析显示,每句子的形容词使用量增加.
- LIWC的分析表明,在治疗后期,积极情绪表达的增加.
结论:
- 从非结构化的EHR文本中,LLM可以有效地提取临床相关的症状信息.
- 这种方法证明了LLM能够捕捉治疗引起的精神病症状变化的能力.
- 该方法提供了一个低负担的方法来支持使用常规EHR数据的临床判断.
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