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

TL;DR

This paper introduces a personalized pathology testing method for cardiovascular disease using an advanced Bayesian inference approach to better account for individual variability and disease stages.

Contribution

It develops a stochastic platelet deposition model combined with approximate Bayesian computation and discriminative summary statistics for personalized CVD diagnosis.

Findings

Successfully estimates biologically meaningful parameters from patient data.

Differentiates between healthy and various patient types based on inferred parameters.

Enables personalized understanding of platelet dysfunction in CVD.

Abstract

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.