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相关实验视频

Updated: Jun 21, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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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
PubMed
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.

关键词:
前期培训和微调工作.真实世界的患者数据.治疗效果估计 治疗效果估计

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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.