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准确的治疗效果估计使用治疗权重的逆概率与深度学习

Junghwan Lee¹, Simin Ma¹, Nicoleta Serban¹

¹H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States.

|April 28, 2025

概括

深度学习模型使用电子健康记录的治疗权重逆概率 (IPTW) 准确估计治疗效果. 这些模型在没有手动功能工程的情况下克服了时间依赖的混,改进了传统方法.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
因果推理因果推理

背景情况:

电子健康记录 (EHR) 越来越多地用于从观察数据中估计治疗效果.
纵向EHR数据往往含有时间依赖的混,复杂无偏见的治疗效果估计.
治疗权重的逆概率 (IPTW) 是一种常用的倾向性得分方法,用于不偏见的估计.

研究的目的:

在使用索赔数据的时间依赖混的情况下,应用IPTW进行治疗效果估计.
利用深度序列模型从索赔记录中直接进行倾向性得分估计,绕过手动特征处理.
评估深度学习模型的性能与IPTW的传统方法相比.

主要方法:

利用深度序列模型,包括循环神经网络和变压器,直接从索赔记录中估计倾向性得分.
应用IPTW与从深度序列模型获得的倾向分数.
基于深度学习的IPTW与逻辑回归和多层感知子的性能进行了比较,使用合成和半合成数据集的特征处理.

主要成果:

针对IPTW的深度序列模型始终优于基线方法 (逻辑回归,具有特征处理的MLP).
拟议的方法在治疗效果估计方面表现出卓越的准确性,特别是在时间依赖的混情景中.
基于变压器的模型通过注意力权重突出相关的混因素来提供可解释性.

结论:

深度序列模型可以通过IPTW直接从索赔数据中准确有效地估计治疗效果.
这些模型消除了对域专业知识和资源密集型特征处理的需求.
深度学习为医疗保健中的因果推理提供了一个强大的,可解释的方法.

关键词:

深度学习是一种深度学习.电子健康记录是电子健康记录.倾向性得分是指倾向性得分.治疗效果估计治疗效果估计

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