从文本到知识:临床信息的端到端提取管道
Mário Macedo1,2, Joshua Wiedekopf3, Tobias Hillmer1,2
1Institute for Medical Informatics and Statistics, Kiel University and University Hospital Schleswig-Holstein, Kiel, Germany.
Studies in health technology and informatics
|August 8, 2025
概括
大型语言模型 (LLM) 显示出从德国临床文本中提取过敏数据的潜力. 虽然对于物质检测是准确的,但反应识别需要进一步开发用于医疗保健应用的LLM.
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.
背景情况:
- 临床免费文本包含有价值的患者数据,但提取结构化的信息是具有挑战性的.
- 自动提取过敏信息对于患者安全和临床决策至关重要.
研究的目的:
- 评估开源大型语言模型 (LLM) 在从德国临床自由文本中提取和结构化过敏反应数据的有效性.
- 开发和测试一个端到端的工作流程,用于过敏数据提取,映射和格式化.
主要方法:
- 在500封匿名的德国释放信中使用了开源LLMs (Llama 3.1,Qwen 2.5,Mistral NeMo).
- 开发了一个工作流程来提取过敏信息,将其映射到SNOMED CT代码,并将其格式化为HL7 FHIR资源.
主要成果:
- 在检测和编码过敏物质方面实现了高精度.
- 由于复杂的字符串匹配问题,在反应识别和编码中发现了挑战.
- 不同的LLM对特定任务的性能有所不同.
结论:
- LLM 显示了自动提取特定领域的医疗保健数据的潜力.
- 通过专门的模型培训和先进的快速工程,可以进一步改进.
- 这一概念验证突显了LLM在处理复杂的临床数据方面的实用性,以改善医疗保健自动化.
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