Extracorporeal membrane oxygenation (ECMO) is an essential life-supporting modality for COVID-19 patients who are refractory to conventional therapies. However, the proper treatment decision has been the subject of significant debate and it remains controversial about who benefits from this scarcely available and technically complex treatment option. To support clinical decisions, it is a critical need to predict the treatment need and the potential treatment and no-treatment responses. Targeting this clinical challenge, we propose Treatment Variational AutoEncoder (TVAE), a novel approach for individualized treatment analysis. TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases. TVAE conceptualizes the treatment decision as a multi-scale problem. We model a patient's potential treatment assignment and the factual and counterfactual outcomes as part of their intrinsic characteristics that can be represented by a deep latent variable model. The factual and counterfactual prediction errors are alleviated via a reconstruction regularization scheme together with semi-supervision, and the selection bias and the scarcity of treatment cases are mitigated by the disentangled and distribution-matched latent space and the label-balancing generative strategy. We evaluate TVAE on two real-world COVID-19 datasets: an international dataset collected from 1651 hospitals across 63 countries, and a institutional dataset collected from 15 hospitals. The results show that TVAE outperforms state-of-the-art treatment effect models in predicting both the propensity scores and factual outcomes on heterogeneous COVID-19 datasets. Additional experiments also show TVAE outperforms the best existing models in individual treatment effect estimation on the synthesized IHDP benchmark dataset.

翻译：体外膜肺氧合（ECMO）是对常规治疗无效的COVID-19患者必不可少的生命支持手段。然而，关于谁能从这种稀缺且技术复杂的治疗方案中获益，一直是重大争议的焦点。为支持临床决策，迫切需要预测治疗需求以及潜在治疗与未治疗反应。针对这一临床挑战，我们提出治疗变分自编码器（TVAE），一种用于个体化治疗分析的新方法。TVAE专门设计用于解决ECMO等存在强烈治疗选择偏差和稀缺治疗案例的建模难题。TVAE将治疗决策概念化为一个多尺度问题。我们将患者的潜在治疗分配以及事实与反事实结果建模为其内在特征的一部分，可通过深度潜在变量模型表示。通过重构正则化方案与半监督学习缓解事实与反事实预测误差，并通过解耦且分布匹配的潜在空间以及标签平衡生成策略减轻选择偏差与治疗案例稀缺性。我们在两个真实世界COVID-19数据集上评估了TVAE：一个涵盖63个国家1651家医院的国际数据集，以及一个来自15家医院的机构数据集。结果表明，TVAE在异质性COVID-19数据集上预测倾向得分和事实结果方面均优于最先进的治疗效果模型。额外实验还显示，TVAE在合成IHDP基准数据集上的个体治疗效果估计中优于现有最佳模型。