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Cardio/cerebrovascular diseases (CVD) have become one of the major healthissue in our societies. But recent studies show that the present pathologytests to detect CVD are ineffectual as they do not consider different stages ofplatelet activation or the molecular dynamics involved in platelet interactionsand are incapable to consider inter-individual variability. Here we propose astochastic platelet deposition model and an inferential scheme to estimate thebiologically meaningful model parameters using approximate Bayesian computationwith a summary statistic that maximally discriminates between different typesof patients. Inferred parameters from data collected on healthy volunteers anddifferent patient types help us to identify specific biological parameters andhence biological reasoning behind the dysfunction for each type of patients.This work opens up an unprecedented opportunity of personalized pathology testfor CVD detection and medical treatment.

Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning