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Updated: Jan 13, 2026

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临床试验患者匹配:在癌症临床试验中进行半自动患者预查的实时,常用数据模型和人工智能驱动系统

Guannan Gong^1,2, Jessica Liu¹, Sameer Pandya³

¹Yale Cancer Center, Yale School of Medicine, New Haven, CT.

JCO clinical cancer informatics

|January 9, 2026

概括

一个新的AI和NLP工具简化了癌症临床试验匹配,大大减少了工作量和查时间. 这通过有效地识别符合条件的候选人来改善患者对试验的访问.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
临床试验管理临床试验管理

背景情况:

癌症临床试验的招生率非常低 (5%-7%),这是由于手动患者试验匹配瓶造成的.
现有的AI/ML系统缺乏数据标准化和跨卫生系统兼容性.

研究的目的:

开发和验证一个半自动化的临床试验患者匹配 (CTPM) 工具.
提高临床试验招聘的效率和可扩展性.

主要方法:

创建了一个基于规则和自然语言处理 (NLP) 的混合管道.
将标准化电子健康记录 (EHR) 数据转化为观察医疗结果伙伴关系 (OMOP) 共同数据模型.
对结直肠癌试验的手动图表审查进行了验证CTPM,然后在29个试验中实施.

主要成果:

对于CRC试验,CTPM实现了94%的回顾性准确性和88%的前性准确性,具有100%的灵敏度.
减少了十倍的图表审查工作量和41%的选时间.
在29个试验中选了98,348名患者,确定了825名符合条件的候选人,并促进了117名入学.

结论:

人工智能/NLP工具通过将研究团队专注于合格候选人来提高临床试验招聘效率.
基于OMOP的框架使整个卫生系统的可扩展性成为可能.
解决招生挑战,可能增加患者获得新型癌症治疗的机会.

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