一个模块化管道自然语言处理选的人类抽象的实用试验结果从电子健康记录的模块化管道
Robert Y Lee1,2, Kevin S Li2,3, James Sibley2,4
1Division of Pulmonary, Critical Care, and Sleep Medicine, University of Washington School of Medicine, Seattle, WA, USA.
Clinical trials (London, England)
|February 12, 2026
概括
我们开发了一个结合自然语言处理 (NLP) 和人体审查的可扩展系统,以准确地测量电子健康记录 (EHR) 的临床试验结果. 这一高效的管道改善了用于研究的数据抽象.
科学领域:
- 临床信息学 临床信息学
- 医疗保健服务研究 医疗服务研究
- 计算语言学 计算语言学
背景情况:
- 电子健康记录 (EHR) 包含有价值的临床数据,但为实用性试验提取结果需要高度准确性.
- 自然语言处理 (NLP) 可以有效地选EHR,但可能缺乏复杂结果测量的精度.
- 将NLP与人类判断相结合提供了一个解决方案,但强大的软件支持是有限的.
研究的目的:
- 开发和验证一种模块化,可扩展的NLP选人类抽象系统,以测量临床试验中的初级结果.
- 通过将自动查与专家人体审查相结合,提高EHR衍生结果测量的准确性.
主要方法:
- 一个深度学习NLP模型选了两个住院患者试验中的EHR段落来讨论护理目标.
- 通过基于REDCap的结果测量系统对屏幕正面段落进行了人类判断.
- 在REDCap中的结构化查询语言 (SQL) 动态聚合可以最大限度地减少不必要的数据抽象,同时确保完整性.
主要成果:
- 在试验1 (N=2512) 中,NLP选了260万个EHR段落,确定了22,187个用于裁决. 人类审查员在34.3小时内抽象了7494个段落,达到92.6%的患者级别灵敏度.
- 在第二个试验中 (N=617),NLP选了559596个段落,确定了8952. 人类审查员在27.9小时内抽象了3509个段落,接近100%的灵敏度.
- 该系统有效地测量了结果,证明了EHR衍生数据的可扩展性和准确性.
结论:
- 开发了一种可扩展和高效的管道,用于使用NLP选的人类抽象测量复杂的EHR衍生的结果.
- 模块化设计可以适应各种研究需求,在定制和商业解决方案之间提供实用平衡.
- 这种方法提高了利用EHR数据进行实用性临床试验结果评估的可行性和准确性.
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