使用大型语言模型开发用于甲状腺癌分期和风险级别分类的命名实体框架
Matrix M H Fung1, Eric H M Tang2,3, Tingting Wu2
1Division of Endocrine Surgery, Department of Surgery, School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
NPJ digital medicine
|March 2, 2025
概括
我们使用大型语言模型 (LLM) 创建了一个框架,从甲状腺癌临床笔记中提取癌症阶段和风险信息. 这种方法高效准确地分类了差异很好的甲状腺癌患者.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 对于患者管理来说,分类差异很好的甲状腺癌阶段和风险至关重要.
- 从临床笔记中提取这些信息可能是具有挑战性的,因为半结构化数据.
- 现有的方法在处理大数据集时可能缺乏效率和准确性.
研究的目的:
- 开发一个命名实体 (NE) 框架,从癌症基因组图谱-甲状腺癌 (TCGA-THCA) 数据库中提取信息.
- 评估大型语言模型 (LLM) 用于分类美国癌症联合委员会 (AJCC) 阶段和美国甲状腺协会 (ATA) 风险类别.
- 为了优化分类甲状腺癌阶段和风险的效率和准确性.
主要方法:
- 开发了一个NE框架,包括注释准则,基本真相标签,提示策略和评估代码.
- 使用了四个LLM (Mistral-7B-Instruct,Llama-3.1-8B-Instruct,Gemma-2-9B-Instruct,Qwen2.5-7B-Instruct) 来进行离线信息提取.
- 采用集体式多数投票策略进行分类,通过TCGA-THCA病理说明和伪临床病例进行验证.
主要成果:
- 该NE框架是使用分别50和289个TCGA-THCA笔记开发和验证的.
- 一个整体策略在分类AJCC分期和ATA风险类别方面取得了令人满意的表现.
- 开发的框架和分类器证明了最佳的效率和准确性.
结论:
- 拟议的NE框架和基于LLM的组合分类器有效地提取和分类甲状腺癌的关键临床信息.
- 这种方法提高了确定AJCC分期和ATA风险类别的准确性和效率.
- 这项研究为分析大规模甲状腺癌数据提供了宝贵的工具.
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