Interpretable pathological test for Cardio-vascular disease: Approximate Bayesian computation with distance learning

Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies. But recent studies show that the present clinical tests to detect CVD are ineffectual as they do not consider different stages of platelet activation or the molecular dynamics involved in platelet interactions and are incapable to consider inter-individual variability. Here we propose a stochastic platelet deposition model and an inferential scheme for uncertainty quantification of these parameters using Approximate Bayesian Computation and distance learning. Finally we show that our methodology can learn biologically meaningful parameters, which are the specific dysfunctioning parameters in each type of patients, from data collected from healthy volunteers and patients. This work opens up an unprecedented opportunity of personalized pathological test for CVD detection and medical treatment. Also our proposed methodology can be used to other fields of science where we would need machine learning tools to be interpretable.