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.
Ritabrata Dutta, Karim Zouaoui-Boudjeltia, Christos Kotsalos, Alexandre Rousseau, Daniel Ribeiro de Sousa, Jean-Marc Desmet, Alain Van Meerhaeghe, Antonietta Mira, Bastien Chopard