  Cardio/cerebrovascular diseases (CVD) have become one of the major health
issue in our societies. But recent studies show that the present pathology
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 to estimate the
biologically meaningful model parameters using approximate Bayesian computation
with a summary statistic that maximally discriminates between different types
of patients. Inferred parameters from data collected on healthy volunteers and
different patient types help us to identify specific biological parameters and
hence biological reasoning behind the dysfunction for each type of patients.
This work opens up an unprecedented opportunity of personalized pathology test
for CVD detection and medical treatment.
