从使用大型语言模型的临床笔记中解读物质使用障碍的严重程度
Maria Mahbub1, Gregory M Dams2, Sudarshan Srinivasan3
1Oak Ridge National Laboratory, Oak Ridge, TN, USA. mahbubm@ornl.gov.
Npj mental health research
|February 6, 2025
概括
大型语言模型 (LLM) 可以有效地从临床笔记中提取物质使用障碍 (SUD) 的严重程度,优于传统方法. 这一进步有助于对SUD患者进行更好的风险评估和个性化治疗计划.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 精神病学是一个精神病学.
背景情况:
- 药物使用障碍 (SUD) 对健康和社会产生重大影响,需要准确的识别和治疗.
- 目前的诊断编码系统 (例如,ICD-10) 缺乏全面SUD评估所需的细节性.
- 临床笔记包含重要的,细粒度的SUD信息,但对传统的自然语言处理 (NLP) 方法来说很难解析.
研究的目的:
- 调查大语言模型 (LLM) 在从非结构化的临床笔记中提取SUD严重性信息的有效性.
- 开发和评估基于LLM的工作流程,以提高SUD诊断的细节性.
- 改善SUD患者的风险评估和治疗计划.
主要方法:
- 开发了一个新的工作流程,利用零射击学习与精心设计的LLMs提示.
- 开源的LLM,Flan-T5,被用来进行实验.
- 根据基于规则的方法评估绩效,重点关注SUD诊断的11个类别.
主要成果:
- 与基于规则的方法相比,LLM在提取SUD严重性信息方面表现出更好的回忆力.
- 拟议的LLM工作流有效地解析了各种临床语言,克服了传统NLP的局限性.
- 在11个不同的SUD诊断类别中成功提取了严重程度信息.
结论:
- 从临床文本中准确地提取颗粒状SUD严重性数据,LLM显示出显著的希望.
- 这种方法可以增强现有的诊断编码系统的局限性.
- 这些发现支持使用LLM来改善SUD患者护理中的临床决策.
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