用小模型微调提取临床报告结构化信息，效果媲美大模型。
ELMTEX: Fine-Tuning Large Language Models for Structured Clinical Information Extraction. A Case Study on Clinical Reports
- 用提示工程和微调结合的方式处理非结构化临床报告。
- 微调后的小型模型在性能上超过大型模型，效率更高。
- 适合医疗数据少、资源有限的机构使用。
欧洲医疗系统亟需提升互操作性与数字化水平，推动对处理遗留临床数据创新方案的需求。本文介绍项目成果，旨在利用大语言模型（LLM）从非结构化临床报告中提取患者病史、诊断、治疗等预定义类别的结构化信息。我们设计了包含用户界面的工作流，并通过提示策略与微调评估了不同规模的LLM。结果表明，微调后的较小模型在性能上可匹配甚至超越大型模型，适用于资源受限场景。新构建的数据集包含60,000条英文临床摘要及24,000条德语翻译，经自动与人工验证。评估采用ROUGE、BERTScore和实体级指标。研究验证了该方法的可行性，并提出未来优化方向。
原文摘要 · Abstract (English)
Europe's healthcare systems require enhanced interoperability and digitalization, driving a demand for innovative solutions to process legacy clinical data. This paper presents the results of our project, which aims to leverage Large Language Models (LLMs) to extract structured information from unstructured clinical reports, focusing on patient history, diagnoses, treatments, and other predefined categories. We developed a workflow with a user interface and evaluated LLMs of varying sizes through prompting strategies and fine-tuning. Our results show that fine-tuned smaller models match or surpass larger counterparts in performance, offering efficiency for resource-limited settings. A new dataset of 60,000 annotated English clinical summaries and 24,000 German translations was validated with automated and manual checks. The evaluations used ROUGE, BERTScore, and entity-level metrics. The work highlights the approach's viability and outlines future improvements.
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