Assisting Clinical Decisions for Scarcely Available Treatment via
  Disentangled Latent Representation
Bing Xue Ahmed Sameh Said Ziqi Xu Hanyang Liu Neel Shah Hanqing Yang Philip Payne Chenyang Lu
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.