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The use of mechanistic models in clinical studies is limited by the lack ofmulti-modal patients data representing different anatomical and physiologicalprocesses. For example, neuroimaging datasets do not provide a sufficientrepresentation of heart features for the modeling of cardiovascular factors inbrain disorders. To tackle this problem we introduce a probabilistic frameworkfor joint cardiac data imputation and personalisation of cardiovascularmechanistic models, with application to brain studies with incomplete heartdata. Our approach is based on a variational framework for the joint inferenceof an imputation model of cardiac information from the available features,along with a Gaussian Process emulator that can faithfully reproducepersonalised cardiovascular dynamics. Experimental results on UK Biobank showthat our model allows accurate imputation of missing cardiac features indatasets containing minimal heart information, e.g. systolic and diastolicblood pressures only, while jointly estimating the emulated parameters of thelumped model. This allows a novel exploration of the heart-brain jointrelationship through simulation of realistic cardiac dynamics corresponding todifferent conditions of brain anatomy.

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