通过微调嵌入合成数据,通过大型语言模型增强瘤专用问题答案
Kun-Han Lu1,2, Sina Mehdinia1,2, Kingson Man1,2
1Department of Applied AI and Data Science, City of Hope, Duarte, CA.
JCO clinical cancer informatics
|September 5, 2025
概括
这项研究引入了瘤电子健康记录 (EHR) 的增强检索增强生成 (RAG) 模型. 新模型显著提高了癌症相关查询的临床笔记的准确性和相关性.
科学领域:
- 医疗信息学
- 自然语言处理
- 癌症数据提取
背景情况:
- 检索增强生成 (RAG) 和大型语言模型 (LLM) 的进步已经改变了电子健康记录 (EHR) 的现实世界证据提取.
- 从非结构化瘤电子病历中提取精确的临床信息仍然是一个挑战,影响研究和患者护理.
研究的目的:
- 通过开发专门的猎犬编码器,提高瘤电子健康记录的RAG有效性.
- 提高在瘤学特定查询中检索的临床笔记的准确性和相关性.
主要方法:
- 通过对209135名患者的600多万份瘤病记进行预训练.
- 微调模型作为一个句子转换器使用12371个LLM合成的查询段对.
- 在50个瘤问题上使用NDCG,精度和回忆指标对六个嵌入模型进行检索性能评估.
主要成果:
- 开发的模型在NDCG中表现优于亚军,9%,在精密性方面表现优于亚军,7%,在回忆方面表现优于亚军 (10项结果).
- 在所有关键瘤学类别 (如诊断,疾病状况和瘤特征) 中观察到异常的检索性能.
- 该模型在从瘤电子病历中获取相关的临床记录方面表现出卓越的能力.
结论:
- 预先训练有素的语境嵌入和句子转换器对于检索相关的瘤电子健康记录是有效的.
- 用LLM合成的查询通道对为专业领域提供了可行的数据增强策略.
- 这种微调方法有望改善医疗保健环境中的数据提取,但注释数据有限.
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