SynthEHR-驱逐:通过LLM增强的合成EHR数据增强驱逐SDoH检测
Zonghai Yao1,2, Youxia Zhao2, Avijit Mitra1,2
1Center for Healthcare Organization and Implementation Research, VA Bedford Health Care, MA, USA.
medRxiv : the preprint server for health sciences
|August 12, 2025
概括
我们开发了SynthEHR-Eviction,这是一个从临床笔记中提取驱逐数据的管道. 该工具创建了与驱逐相关的健康社会决定因素的最大公共数据集,改善了健康研究.
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.
- 健康的社会决定因素
背景情况:
- 驱逐是一个研究不足的健康社会决定因素 (SDoH),与不良健康结果相关.
- 驱逐数据通常在电子健康记录 (EHR) 中没有结构,这阻碍了其在研究和临床实践中的使用.
研究的目的:
- 引入SynthEHR-Eviction,这是一个可扩展的管道,用于从临床笔记中提取驱逐状态.
- 使用开发的管道创建与驱逐有关的SDoH最大的公共数据集.
- 在此数据集上评估微调的大型语言模型 (LLM) 的性能.
主要方法:
- 开发了SynthEHR-Eviction,这是一个管道,集成了LLMs,人在循环注释和自动提示优化 (APO).
- 创建了一个与驱逐有关的SDoH的公共数据集,包含14个细粒度的类别.
- 训练并评估精细调整的LLM (Qwen2.5,LLaMA3) 并将其与其他模型 (GPT-4o-APO,GPT-4o-mini-APO,BioBERT) 进行比较.
主要成果:
- 在SynthEHR-Eviction上接受培训的精心调整的LLM获得了高的宏观F1分数:88%的驱逐和90.3%的其他SDoH.
- 与GPT-4o-APO,GPT-4o-mini-APO和BioBERT相比,该管道表现出更高的性能.
- 标注工作量减少了80%以上,加速了数据集的创建.
结论:
- 通过SynthEHR-Eviction,可以从临床笔记中以可扩展和成本效益的方式检测与驱逐有关的SDoH.
- 开发的数据集和微调的模型推动了对驱逐的健康影响的研究.
- 该管道的方法可用于医疗保健中的其他信息提取任务.
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