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Deep multiphysics: Coupling discrete multiphysics with machine learning to attain self-learning in-silico models replicating human physiology.

Abstract：

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.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Alexiadis A

doi

10.1016/j.artmed.2019.06.005

subject

Has Abstract

pub_date

2019-07-01 00:00:00

pages

27-34

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(18)30585-2

journal_volume

98

pub_type

杂志文章

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