向临床试验有效招募患者:应用基于即时的学习模型
Mojdeh Rahmanian1, Seyed Mostafa Fakhrahmad1, Seyedeh Zahra Mousavi2
1Department of Computer Science and Engineering and IT, Shiraz University, Shiraz, Iran.
Healthcare informatics research
|November 20, 2025
概括
使用基于提示的大型语言模型 (LLM) 和本体学驱动的总结的新方法显著改善了从非结构化的医疗笔记中识别临床试验参与者的能力,实现了高性能指标.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 医疗保健中的人工智能
- 临床试验招聘 临床试验招聘
背景情况:
- 鉴定符合条件的临床试验参与者是一个主要的挑战,因为复杂的,非结构化的医学文本.
- 基于变压器的模型为队列选择中的这个瓶提供了潜在的解决方案.
研究的目的:
- 评估基于提示的大型语言模型 (LLM) 来从非结构化的临床笔记中进行队列选择.
- 为了评估LLM的表现,与本体学驱动的总结集成,用于资格分类.
主要方法:
- 医疗记录使用医学临床术语系统化命名法 (SNOMED CT) 进行注释.
- 由本体学驱动的总结提取了符合资格标准的相关句子.
- 一个基于提示的LLM (GPT-3.5-turbo) 在零射击设置中对标准进行了分类.
- 在2018年n2c2数据集 (288名患者,13个标准) 上评估了性能.
主要成果:
- 基于提示的模型实现了高微 (0.9061) 和宏 (0.8060) 的F-措施.
- 这些得分代表了n2c2数据集中报告的一些最高得分.
结论:
- 将基于本体学的总结与基于提示的LLM集成,可以提高资格分类的准确性.
- 该管道为临床试验自动化和EMR集成提供了一个可扩展的框架.
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