在多中心临床试验中判断心力衰竭的自然语言处理:随机临床试验的二次分析
Jonathan W Cunningham1,2, Pulkit Singh3, Christopher Reeder3
1Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, Massachusetts.
JAMA cardiology
|November 11, 2023
概括
自然语言处理 (NLP) 模型在多中心试验中对心力衰竭 (HF) 住院裁决表现出很好的一致性,验证了它们作为医生审查的资源高效替代品的使用.
科学领域:
- 临床试验 临床试验
- 医疗信息学 医疗信息学
- 心脏病学 心脏病学
背景情况:
- 医生领导的临床事件委员会 (CEC) 是临床试验结果裁决的黄金标准,但资源密集.
- 使用自然语言处理 (NLP) 的自动裁决提供了一个潜在的替代方案,但需要在多中心环境中进行验证.
研究的目的:
- 在多中心临床试验中,对心力衰竭 (HF) 住院裁决的社区护理队列项目 (C3PO) NLP模型进行外部验证.
- 将NLP模型的性能与黄金标准CEC判断进行比较.
主要方法:
- 对流感疫苗进行回顾性分析,以有效地阻止心胸事件和失偿心力衰竭 (INVESTED) 试验数据.
- 通过中央INVESTED CEC和C3PO NLP模型对住院的独立裁决.
- 微调C3PO NLP模型并使用INVESTED试验数据训练一个新的NLP模型.
主要成果:
- C3PO NLP 模型与 CEC 的裁决表现出很好的一致性 (原始协议, 87%; κ, 0.69).
- 该模型实现了高灵敏度 (94%) 和特异性 (84%) 的HF住院鉴定.
- 微调和 de novo NLP 模型显示了与 CEC 的更好的一致性 (93%及以上),接近人类间的可重复性 (94%).
结论:
- C3PO NLP 模型有效地在多中心试验中裁决高压住院治疗,支持其作为CEC资源高效替代品的使用.
- 模型微调增强了协议,并接近了人类的可重复性.
- 需要进一步的研究来评估NLP在未来的多中心试验中识别临床事件的效率.
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