使用基于共识的推理和大型语言模型从外科病理报告中提取结构化数据
Aakash Tripathi1, Asim Waqas2, Kavya Venkatesan1
1Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL.
一个使用多个大型语言模型 (LLM) 的新框架准确地从病理学报告中提取癌症数据. 这种方法改善了用于癌症分期和治疗计划的数据分析.
科学领域:
- 医疗信息学 医疗信息学
- 计算病理学计算病理学
- 人工智能在医学中的应用
背景情况:
- 手术病理学报告包含关键的癌症诊断信息,但在格式和风格上有很大差异.
- 这些报告的非结构化性质阻碍了用于大规模分析的自动数据提取.
- 跨瘤类型和机构的变化为一致的数据检索带来了重大挑战.
研究的目的:
- 从病理学报告中提取标准诊断变量和生物标志物的共识驱动,基于推理的框架.
- 适应本地部署的大型语言模型 (LLM) 进行准确可靠的数据提取.
- 评估框架在不同器官系统和癌症类型中的表现.
主要方法:
- 利用多个本地部署的大型语言模型 (LLM) 来提取诊断变量 (位置,组织学,阶段,等级,行为) 和生物标志物.
- 采用了三个独立的推理模型来评估LLM产生的输出的准确性和连贯性.
- 汇总的输出以确定最终的共识值,并由董事会认证的病理学家进行专家验证.
主要成果:
- 该框架在从超过6100份癌症基因组图谱 (TCGA) 报告 (平均84.9%±7.3%) 和510份莫菲特癌症中心报告 (平均88.2%±7.2%) 中提取标准变量时取得了高准确度.
- 组织学,部位和行为显示出最高的提取精度,专家审查证实了关键变量之间的强烈一致.
- 生物标志物提取实现了70.6%±7.9%的整体准确度,特定的生物标志物在相关瘤类型中表现高.
结论:
- 在基于共识的框架内,本地部署的LLM提供了用于病理学数据提取的透明,准确和可审计的解决方案.
- 该框架展示了将其整合到现实世界工作流程中的潜力,例如综合报告和癌症注册表抽象.
- 多层次,多器官评估框架与多评估者共识对于临床应用中的LLM的基准测试至关重要.
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