Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets

TL;DR

This paper presents a probabilistic framework that jointly imputes missing cardiac data and personalizes cardiovascular models to better understand heart-brain interactions in incomplete clinical datasets.

Contribution

It introduces a novel variational approach combining data imputation with mechanistic modeling for cardiovascular analysis in brain studies.

Findings

Accurately imputes missing cardiac features from minimal data.

Enables simulation of personalized cardiac dynamics.

Facilitates exploration of heart-brain relationships.

Abstract

The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart features for the modeling of cardiovascular factors in brain disorders. To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features, along with a Gaussian Process emulator that can faithfully reproduce personalised cardiovascular dynamics. Experimental results on UK Biobank show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information, e.g. systolic and diastolic blood pressures only, while jointly estimating the emulated parameters of the lumped model. This allows a novel exploration of the heart-brain joint relationship through simulation of realistic cardiac dynamics corresponding to different conditions of brain anatomy.