德国临床文本中ICD-10编码的检索增强生成 - 技术案例报告
Mario Krumscheid1,2, Johannes Blömer3, Matthias Becker1,2
1Department of Computer Science, Kempten University of Applied Sciences, Kempten, Germany.
Studies in health technology and informatics
|September 3, 2025
概括
这项研究引入了德国临床文本的国际疾病分类 (ICD) 编码的半自动化管道,提高了医疗文档的效率和准确性.
科学领域:
- 医疗信息学
- 自然语言处理
- 临床文档
背景情况:
- 德国临床文本的手册国际疾病分类 (ICD) 编码效率低下,容易出现错误.
- 现有的自动化方法难以使用低资源语言和稀有代码.
研究的目的:
- 开发一个半自动化管道,以高效地编码非结构化的德国医疗文档.
- 解决目前在德国临床文本分析中的微调语言模型的局限性.
主要方法:
- 实施了获取增强生成 (RAG) 框架,包括命名实体识别,语义检索和缩写解析.
- 使用Sentence-BERT嵌入,FAISS索引,以及用于ICD代码分配的Mistral-Small-Instruct生成模型.
- 综合语义相似性和生成精细化为准确的代码选择.
主要成果:
- 开发的管道集成了多种NLP技术用于半自动化ICD编码.
- 确定语义检索和诊断规范化为减少错误的关键领域.
- 展示了一种处理低资源临床文本编码挑战的新方法.
结论:
- 该RAG管道为德语半自动化ICD编码提供了一个有前途的方法.
- 未来的工作应该加强特定领域的嵌入和上下文的提示,以提高准确性.
- 该系统旨在提高临床编码在现实环境中的准确性和可用性.
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