2020/10/13 by Ritabrata Dutta, Dutta, Ritabrata, Karim Zouaoui-Boudjeltia +15
Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (stat.ML) #Machine Learning and Algorithms #Markov Chains and Monte Carlo Methods #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2010.06465
openalex publication_date 2020/10/13 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.