使用基于共识的推理与本地部署的LLM来实现从外科病理报告中提取结构化数据
Aaksh Tripathi1, Asim Waqas2, Kavya Venkatesan1
1Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute.
概括
一个使用大型语言模型 (LLM) 的新框架准确地从非结构化的病理报告中提取关键的癌症诊断信息. 这种人工智能驱动的方法增强了用于癌症分期和注册表文档的数据提取.
科学领域:
- 计算病理学计算病理学
- 医疗保健中的人工智能
- 对于临床数据的自然语言处理.
背景情况:
- 手术病理学报告对于癌症诊断,分期和治疗计划至关重要.
- 这些报告的自由文本性质和可变性阻碍了自动数据提取.
- 准确提取诊断变量对于癌症注册表和临床决策至关重要.
研究的目的:
- 开发和评估一个基于共识,基于推理的框架,使用本地部署的大型语言模型 (LLM) 来从外科病理报告中提取关键诊断变量.
- 评估LLM驱动的数据提取在各种瘤类型和机构的准确性和可解释性.
- 为整合AI进入病理学工作流程提供透明和可审计的解决方案.
主要方法:
- 一个使用多个本地部署的LLM来提取六个关键变量的框架:位置,横向性,组织学,阶段,等级和行为.
- 每个LLM输出都包括了理由,这些理由通过单独的推理模型来评估准确性和连贯性.
- 总结了共识值,专家病理学家对TCGA和莫菲特癌症中心的4000多份报告的结果进行了验证.
主要成果:
- 在TCGA数据集中,对于行为 (100.0%),组织学 (98.5%),部位 (95.2%) 和等级 (95.6%) 达成了高度一致.
- 在TCGA.中,阶段 (87.6%) 和横向性 (84.8%) 的表现较低.
- 莫菲特 (大脑,乳房,肺部) 的病理学报告显示,组织学 (95.6%),行为 (98.3%) 和阶段 (92.4%) 的准确性很高.
- 挑战包括不一致的哨兵淋巴结细节和解剖学模两可.
- 统计分析显示,模型类型,变量和器官系统对性能有显著影响.
结论:
- 在基于共识的框架内,本地部署的LLM提供了一个透明,准确和可审计的解决方案,用于从病理学报告中提取关键诊断信息.
- 这种人工智能驱动的方法可以改善癌症注册表抽象和综合报告.
- 分层,多器官评估框架对于临床应用中LLM的基准测试至关重要.
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