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In Artificial intelligence in medicine

OBJECTIVES : The objective of this study is to devise a modelling strategy for attaining in-silico models replicating human physiology and, in particular, the activity of the autonomic nervous system.

METHOD : Discrete Multiphysics (a multiphysics modelling technique) and Reinforcement Learning (a Machine Learning algorithm) are combined to achieve an in-silico model with the ability of self-learning and replicating feedback loops occurring in human physiology. Computational particles, used in Discrete Multiphysics to model biological systems, are associated to (computational) neurons: Reinforcement Learning trains these neurons to behave like they would in real biological systems.

RESULTS : As benchmark/validation, we use the case of peristalsis in the oesophagus. Results show that the in-silico model effectively learns by itself how to propel the bolus in the oesophagus.

CONCLUSIONS : The combination of first principles modelling (e.g. multiphysics) and machine learning (e.g. Reinforcement Learning) represents a new powerful tool for in-silico modelling of human physiology. Biological feedback loops occurring, for instance, in peristaltic or metachronal motion, which until now could not be accounted for in in-silico models, can be tackled by the proposed technique.

Alexiadis Alessio


Coupling first-principles models with machine learning, Discrete multiphysics, Particle-based computational methods, Reinforcement Learning