用大型语言模型提高临床笔记中的物质使用检测
Research square
|June 5, 2025
概括
大型语言模型 (LLM) 可以有效地在电子健康记录 (EHR) 中识别物质使用行为. 一个微调的LLM在检测各种物质使用类别,包括阿片类药物滥用等方面取得了很高的准确性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 公共卫生监督 公共卫生监督
背景情况:
- 在电子健康记录 (EHR) 中识别物质使用是很困难的,因为非结构化的临床笔记,各种术语和否定.
- 准确的检测对于患者护理,临床决策支持和对物质使用行为的公共卫生监测至关重要.
研究的目的:
- 开发和评估大型语言模型 (LLM) 以检测EHR排放摘要中的八种物质使用类别.
- 为药物检测创建一个大型的注释数据集,以支持系统性物质使用监测.
主要方法:
- 使用MIMIC-III/IV排放摘要构建一个注释的药物检测数据集.
- 研究了多个LLM在零射击,少数射击和微调设置中的性能.
- 评估了检测个人物质使用,处方阿片类药物滥用和多重物质使用的模型.
主要成果:
- 一个微调的LLM,Llama-DrugDetector-70B,表现出卓越的性能.
- 在大多数个体物质使用类别中获得了近乎完美的F1分数 (>=0.95).
- 在处方阿片类药物滥用 (F1=0.815) 和多重物质使用 (F1=0.917) 方面表现强.
结论:
- LLM显著提高了在EHR中检测物质使用行为的能力.
- 精心调整的LLM在临床决策支持和物质使用监测研究方面表现有前途.
- 需要进一步的研究来解决LLM应用程序在这个领域的可扩展性.
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