Graph Autoencoder and StrNN based Causal Analysis of Mortality in Heart Failure Patients

Abstract

Though analyzed for decades, dissecting and finding mechanisms of cardiovascular diseases, especially heart failures, are still an on-going task for many researchers. However, through recent floods of machine learning and deep learning algorithms to replace traditional approaches, and their applications in diverse cardiovascular research areas, it seems plausible to say that conquering or preventing heart failure catastrophes might no longer be a delusional task within a few more years. To accelerate the arrival of a new era, this research implemented several cutting-edge algorithms currently introduced in causal deep learning to observational heart disease patient data to find key mechanisms that lead to cardiac deaths under a highly flexible framework. Extracting latent causal DAGs from observational data using Graph Auto Encoder, and finding specific causal relationships and interventional effects under Structured Neural Networks (StrNN), novel findings regarding key causes of deaths in heart failure patients were found in numerous aspects. Specifically, existence of intervals where average treatment effects due to causal interventions in platelets, ejection fraction, and serum creatinine levels dramatically decrease or increase was found among heart patients, which can lead to significant eliminations or additions of practical clinical treatments in terms of reducing cardiac death event probability after cardiac failure.