提示表:用大型语言模型进行临床信息提取的规范和代精细化
David Hein1, Alana Christie2, Michael Holcomb1
1University of Texas Southwestern Medical Center (UTSW) Lyda Hill Department of Bioinformatics.
medRxiv : the preprint server for health sciences
|February 24, 2025
概括
大型语言模型 (LLM) 能够从脏瘤的病理学报告中提取准确的数据. 这种新的管道有效地结构复杂的临床数据,改善癌症研究.
科学领域:
- 计算病理学计算病理学
- 医学中的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 从非结构化的临床文本中提取结构化数据是一项挑战.
- 传统方法在复杂的医学领域面临局限性.
- 病理学报告包含癌症诊断和研究的重要信息.
研究的目的:
- 开发和验证一种新的管道,用于从使用大型语言模型 (LLM) 的非结构化病理报告中准确地提取和规范化信息.
- 最初关注脏瘤报告,并证明适应性其他癌症类型.
- 在规模上从自由文本医疗记录中生成分析准备的表格数据.
主要方法:
- 一个端到端的管道利用LLM进行信息提取和规范化.
- 灵活的提示模板和直接生成表格数据.
- 一个由错误本体学指导的人在循环中的代改进过程.
- 验证了2,297份脏瘤报告和公开可用的乳腺癌和前列腺癌报告.
主要成果:
- 在脏瘤亚型中达到0.99的宏观平均F1得分,在脏转移检测中达到0.97.
- 通过多个LLM骨干来证明灵活性.
- 展示了适应新领域的适应性 (乳腺癌和前列腺癌报告).
结论:
- 基于LLM的管道为从复杂的临床文本中提取结构化数据提供了高度准确和高效的解决方案.
- 开发的管道成功地结构了病理学报告中的关键信息,促进了分析和研究.
- 强调任务定义,跨学科合作和临床LLM工作流程中的复杂性管理的重要性.
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