  Objective: Modern medicine needs to shift from a wait and react, curative
discipline to a preventative, interdisciplinary science aiming at providing
personalised, systemic and precise treatment plans to patients. The aim of this
work is to present how the integration of machine learning approaches with
mechanistic computational modelling could yield a reliable infrastructure to
run probabilistic simulations where the entire organism is considered as a
whole. Methods: We propose a general framework that composes advanced AI
approaches and integrates mathematical modelling in order to provide a
panoramic view over current and future physiological conditions. The proposed
architecture is based on a graph neural network (GNNs) forecasting clinically
relevant endpoints (such as blood pressure) and a generative adversarial
network (GANs) providing a proof of concept of transcriptomic integrability.
Results: We show the results of the investigation of pathological effects of
overexpression of ACE2 across different signalling pathways in multiple tissues
on cardiovascular functions. We provide a proof of concept of integrating a
large set of composable clinical models using molecular data to drive local and
global clinical parameters and derive future trajectories representing the
evolution of the physiological state of the patient. Significance: We argue
that the graph representation of a computational patient has potential to solve
important technological challenges in integrating multiscale computational
modelling with AI. We believe that this work represents a step forward towards
a healthcare digital twin.
