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

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 inte...

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Veröffentlicht in:PLoS computational biology Jg. 18; H. 3; S. e1009910
Hauptverfasser: Dutta, Ritabrata, Zouaoui Boudjeltia, Karim, Kotsalos, Christos, Rousseau, Alexandre, Ribeiro de Sousa, Daniel, Desmet, Jean-Marc, Van Meerhaeghe, Alain, Mira, Antonietta, Chopard, Bastien
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 10.03.2022
Public Library of Science (PLoS)
Schlagworte:
Bayes Theorem
Bayesian analysis
Bayesian statistical decision theory
Biology and Life Sciences
Blood platelets
Cardiovascular diseases
Cardiovascular Diseases - diagnosis
Cerebrovascular diseases
Chemical vapor deposition
Chronic obstructive pulmonary disease
Computation
Customization
Datasets
Development and progression
Diagnosis
Engineering and Technology
Estimates
Hemodialysis
Humans
Learning
Machine learning
Mathematical models
Medical treatment
Medicine and Health Sciences
Molecular dynamics
Parameter identification
Pathology
Patients
Physical Sciences
Platelets
Statistical tests
Statistics
Stochasticity
Vascular Diseases
United Kingdom
ISSN:1553-7358, 1553-734X, 1553-7358
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Zusammenfassung: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.
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The authors have declared that no competing interests exist.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1009910