  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.
