使用OpenAI InstructGPT从医疗病例报告中提取信息
Veronica Sciannameo1, Daniele Jahier Pagliari2, Sara Urru3
1Centre for Biostatistics, Epidemiology and Public Health, Department of Clinical and Biological Sciences, University of Turin, Regione Gonzole 10, Orbassano 10043, Italy.
Computer methods and programs in biomedicine
|July 19, 2024
概括
像 InstructGPT 这样的大型语言模型 (LLM) 在从医疗病例报告中提取患者数据方面表现出高准确度. 这项技术为从非结构化文本中检索临床信息提供了一个有希望的无代码解决方案.
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.
- 人工智能的人工智能
背景情况:
- 像自然语言处理 (NLP) 这样的自动化解决方案与临床文本的复杂性作斗争.
- 大型语言模型 (LLM) 为非结构化临床数据提供了一个潜在的解决方案.
- 由GPT-3衍生出来的instructGPT被评估用于临床信息提取.
研究的目的:
- 评估 InstructGPT 在从病例报告中提取患者信息方面的表现.
- 将LLM与临床数据提取传统NLP方法的有效性进行比较.
主要方法:
- 在PubMed,Scopus和Web of Science搜索了208份关于儿科外体损伤的病例报告.
- 手动提取患者数据 (性别,年龄,物体,受伤的身体部位) 来创建一个黄金标准.
- 将 InstructGPT 的提取精度与黄金标准进行了比较.
主要成果:
- InstructGPT实现了高精度:94%的性别,82%的年龄,94%的对象和89%的身体部位.
- 除了不可回收的物品,准确性提高到97%的性别/年龄和93%的身体部位.
- InstructGPT成功地从非英语文章中提取了信息.
结论:
- LLM 能够从非结构化文本 (如病例报告) 中快速提取临床信息.
- 基于PDF的提取不需要预处理或技术NLP/ML专业知识.
- 这项研究表明,LLM具有从各种科学文献中有效检索临床数据的潜力.
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