变异性时间解混器网络用于与纵向观测数据的个性化治疗效果估计
Hao Dai1, Yu Huang1, Yuxi Liu1
1Department of Biostatistics & Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA.
Journal of biomedical informatics
|July 23, 2025
概括
这项研究引入了变异时间解惑器网络 (VTDNet) 来估计电子健康记录的个性化治疗效果,有效地处理个性化医学的隐藏混.
科学领域:
- 生物医学信息学 生物医学信息学
- 机器学习 机器学习
- 因果推理因果推理
背景情况:
- 从电子健康记录 (EHR) 数据中估计个性化治疗效应 (ITE) 对个性化医学至关重要.
- 现实世界的数据带来了诸如隐藏混和动态治疗方案等挑战.
- 现有的方法难以应对纵向观测数据的复杂性.
研究的目的:
- 开发一个新的框架来估计ITE在使用EHR数据的纵向观测设置.
- 解决隐藏混和动态治疗方案所带来的统计挑战.
- 通过准确估计治疗效果,推进个性化医疗.
主要方法:
- 提出变异时间解误器网络 (VTDNet),一个使用变异循环变压器自动编码器的框架.
- VTDNet包含一个时间编码解码器,一个治疗相互依赖的治疗区块,以及一个潜在的结果区块来预测结果.
- 在合成,MIMIC-III (重症监护) 和NACC (神经退行性疾病) 数据集上进行的验证.
主要成果:
- 在不同的混水平下,VTDNet在合成数据上表现出卓越的准确性.
- 在现实世界EHR数据集上,VTDNet实现了较低的根平均平方误差和平均绝对误差.
- 与最先进的方法相比,VTDNet在估计异质治疗效果时显示出更好的影响函数精度.
结论:
- VTDNet为纵向设置中的ITE估计提供了一个强大的框架,处理不规则的时间点和高维数据.
- 深度生成方法有效地解决了隐藏的混因素,推进了个性化医学和现实世界的证据生成.
- 未来的工作包括将VTDNet扩展到持续治疗场景,如剂量反应分析.
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