Emulating complex simulations by machine learning methods

• Main Authors: Castiglione, F. (Author), Stolfi, P. (Author)
• Format: Article
• Language: English
• Published: BioMed Central Ltd 2021
• Subjects: algorithm; Algorithms; Complex biological systems; Complex simulation; Computational costs; Computational modelling; Diabetes Mellitus, Type 2; Emulation; Forecasting; human; Humans; Learning algorithms; machine learning; Machine learning; Machine Learning; Machine learning methods; Mean square error; Metabolism; non insulin dependent diabetes mellitus; Ordinary differential equations; Risk assessment; Risk prediction; Risk predictions; Self-assessment; Self-monitoring; Simulators; Type-2 diabetes
• Online Access: View Fulltext in Publisher

 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. © 2021, The Author(s).