论文标题
将因果影响纳入对EHR数据的深度学习预测
Incorporating Causal Effects into Deep Learning Predictions on EHR Data
论文作者
论文摘要
电子健康记录（EHR）数据分析在医疗系统质量中起着至关重要的作用。由于其高度复杂的因果关系和有限的可观察性质，因此对EHR的因果推断非常具有挑战性。深度学习（DL）在先进的机器学习方法中取得了巨大的成功。然而，它仍然被不合适的因果条件所阻碍。这项工作提出了一种新的方法，可以将临床定义明确的因果效应量化为广义估计载体，该估计载体仅适用于因果模型。我们将其纳入DL模型中，以实现更好的预测性能和结果解释。此外，我们还证明了因果信息的存在，普通DL模型无法达到。
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality. Because of its highly complex underlying causality and limited observable nature, causal inference on EHR is quite challenging. Deep Learning (DL) achieved great success among the advanced machine learning methodologies. Nevertheless, it is still obstructed by the inappropriately assumed causal conditions. This work proposed a novel method to quantify clinically well-defined causal effects as a generalized estimation vector that is simply utilizable for causal models. We incorporated it into DL models to achieve better predictive performance and result interpretation. Furthermore, we also proved the existence of causal information blink spots that regular DL models cannot reach.
