治疗:一个深度学习框架,预先训练了大规模的患者数据,以估计治疗效果
Ruoqi Liu1, Pin-Yu Chen2, Ping Zhang1,3,4
1Department of Computer Science and Engineering, The Ohio State University, 2015 Neil Avenue, Columbus, OH 43210, USA.
Patterns (New York, N.Y.)
|July 15, 2024
概括
治疗,一种用于因果治疗效应估计 (TEE) 的新框架,使用未标记的患者数据进行预训练和对标记数据进行微调. 这种方法提高了TEE的准确性,优于现有的方法,并有助于临床试验分析.
科学领域:
- 生物统计学 生物统计学
- 机器学习 机器学习
- 医疗信息学 医疗信息学
背景情况:
- 治疗效果估计 (TEE) 对于识别治疗的因果影响至关重要.
- 目前用于TEE的机器学习方法在有限的标记数据中扎.
- 观测数据对准确的TEE提出了挑战.
研究的目的:
- 提出CURE (因果治疗效应估计),TEE的新型预培训和微调框架.
- 通过大规模未标记的患者数据来提高TEE的性能.
- 开发一种可靠的方法来估计异质治疗效应.
主要方法:
- CURE使用未标记的患者数据进行预训练阶段,以学习上下文表示.
- 使用序列编码方法将结构和时间嵌入到纵向患者数据中.
- 该框架对特定的TEE任务的标记数据进行了微调.
主要成果:
- 在TEE中,CURE显著超过了最先进的方法.
- 在精度回忆曲线下的面积增加了7%.
- 对于估计异质效应的精度提高了8%.
结论:
- 从观察数据中,CURE有效地估计了因果治疗效应.
- 该框架的性能与四项随机临床试验进行了验证.
- CURE显示出可以补充传统临床试验方法的潜力.
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