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Extracorporeal membrane oxygenation (ECMO) is an essential life-supportingmodality for COVID-19 patients who are refractory to conventional therapies.However, the proper treatment decision has been the subject of significantdebate and it remains controversial about who benefits from this scarcelyavailable and technically complex treatment option. To support clinicaldecisions, it is a critical need to predict the treatment need and thepotential treatment and no-treatment responses. Targeting this clinicalchallenge, we propose Treatment Variational AutoEncoder (TVAE), a novelapproach for individualized treatment analysis. TVAE is specifically designedto address the modeling challenges like ECMO with strong treatment selectionbias and scarce treatment cases. TVAE conceptualizes the treatment decision asa multi-scale problem. We model a patient's potential treatment assignment andthe factual and counterfactual outcomes as part of their intrinsiccharacteristics that can be represented by a deep latent variable model. Thefactual and counterfactual prediction errors are alleviated via areconstruction regularization scheme together with semi-supervision, and theselection bias and the scarcity of treatment cases are mitigated by thedisentangled and distribution-matched latent space and the label-balancinggenerative strategy. We evaluate TVAE on two real-world COVID-19 datasets: aninternational dataset collected from 1651 hospitals across 63 countries, and ainstitutional dataset collected from 15 hospitals. The results show that TVAEoutperforms state-of-the-art treatment effect models in predicting both thepropensity scores and factual outcomes on heterogeneous COVID-19 datasets.Additional experiments also show TVAE outperforms the best existing models inindividual treatment effect estimation on the synthesized IHDP benchmarkdataset.

Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation