Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning
This work addresses the need for personalized pathology tests in cardiovascular disease detection and treatment, offering a novel approach to account for inter-individual variability and platelet dynamics, though it is incremental in applying existing computational methods to a specific medical domain.
The authors tackled the problem of ineffective cardiovascular disease detection by developing a stochastic platelet deposition model and an inferential scheme using approximate Bayesian computation with discriminative summary statistics to estimate biologically meaningful parameters from patient data, enabling personalized pathology tests.
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.