Emulating complex simulations by machine learning methods

Abstract Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic an...

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Autores principales:	Paola Stolfi, Filippo Castiglione
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Type-2 diabetes Emulation Computational modelling Risk prediction Self-assessment Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a6a5641db45c41bfb580a4adbb2f8c7d
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spelling	oai:doaj.org-article:a6a5641db45c41bfb580a4adbb2f8c7d2021-11-14T12:13:05ZEmulating complex simulations by machine learning methods10.1186/s12859-021-04354-71471-2105https://doaj.org/article/a6a5641db45c41bfb580a4adbb2f8c7d2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04354-7https://doaj.org/toc/1471-2105Abstract Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic and inflammatory processes underlying the development of type-2 diabetes in absence of familiarity. Given the very high incidence of type-2 diabetes, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of the disease for aware individuals. The high computational cost of the developed model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, makes the simulator executable only on powerful workstations but not on mobile devices. Hence the need to implement an emulator with a reduced computational cost that can be executed on mobile devices to provide real-time self-monitoring. Results Similarly to our previous work, we propose an emulator based on a machine learning algorithm but here we consider a different approach which turn out to have better performances, indeed in terms of root mean square error we have an improvement of two order magnitude. We tested the proposed emulator on samples containing different number of simulated trajectories, and it turned out that the fitted trajectories are able to predict with high accuracy the entire dynamics of the simulator output variables. We apply the emulator to control the level of inflammation while leveraging on the nutritional input. Conclusion The proposed emulator can be implemented and executed on mobile health devices to perform quick-and-easy self-monitoring assessments.Paola StolfiFilippo CastiglioneBMCarticleType-2 diabetesEmulationComputational modellingRisk predictionSelf-assessmentComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss S14, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Type-2 diabetes Emulation Computational modelling Risk prediction Self-assessment Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Type-2 diabetes Emulation Computational modelling Risk prediction Self-assessment Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Paola Stolfi Filippo Castiglione Emulating complex simulations by machine learning methods
description	Abstract Background The aim of the present paper is to construct an emulator of a complex biological system simulator using a machine learning approach. More specifically, the simulator is a patient-specific model that integrates metabolic, nutritional, and lifestyle data to predict the metabolic and inflammatory processes underlying the development of type-2 diabetes in absence of familiarity. Given the very high incidence of type-2 diabetes, the implementation of this predictive model on mobile devices could provide a useful instrument to assess the risk of the disease for aware individuals. The high computational cost of the developed model, being a mixture of agent-based and ordinary differential equations and providing a dynamic multivariate output, makes the simulator executable only on powerful workstations but not on mobile devices. Hence the need to implement an emulator with a reduced computational cost that can be executed on mobile devices to provide real-time self-monitoring. Results Similarly to our previous work, we propose an emulator based on a machine learning algorithm but here we consider a different approach which turn out to have better performances, indeed in terms of root mean square error we have an improvement of two order magnitude. We tested the proposed emulator on samples containing different number of simulated trajectories, and it turned out that the fitted trajectories are able to predict with high accuracy the entire dynamics of the simulator output variables. We apply the emulator to control the level of inflammation while leveraging on the nutritional input. Conclusion The proposed emulator can be implemented and executed on mobile health devices to perform quick-and-easy self-monitoring assessments.
format	article
author	Paola Stolfi Filippo Castiglione
author_facet	Paola Stolfi Filippo Castiglione
author_sort	Paola Stolfi
title	Emulating complex simulations by machine learning methods
title_short	Emulating complex simulations by machine learning methods
title_full	Emulating complex simulations by machine learning methods
title_fullStr	Emulating complex simulations by machine learning methods
title_full_unstemmed	Emulating complex simulations by machine learning methods
title_sort	emulating complex simulations by machine learning methods
publisher	BMC
publishDate	2021
url	https://doaj.org/article/a6a5641db45c41bfb580a4adbb2f8c7d
work_keys_str_mv	AT paolastolfi emulatingcomplexsimulationsbymachinelearningmethods AT filippocastiglione emulatingcomplexsimulationsbymachinelearningmethods
_version_	1718429365630402560

Emulating complex simulations by machine learning methods

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