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Publication . Article . 2020

Mathematical Models of Meal Amount and Timing Variability With Implementation in the Type-1 Diabetes Patient Decision Simulator

Camerlingo, N.; Vettoretti, M.; Del Favero, S.; Facchinetti, A.; Sparacino, G.; on behalf of the Hypo-RESOLVE Consortium,;
Open Access
English
Published: 17 Sep 2020 Journal: Journal of Diabetes Science and Technology, volume 15, issue 2, pages 346-359 (eissn: 1932-2968, Copyright policy )
Publisher: SAGE Publications
Country: Italy
Abstract

Background: In type 1 diabetes (T1D) research, in-silico clinical trials (ISCTs) have proven effective in accelerating the development of new therapies. However, published simulators lack a realistic description of some aspects of patient lifestyle which can remarkably affect glucose control. In this paper, we develop a mathematical description of meal carbohydrates (CHO) amount and timing, with the aim to improve the meal generation module in the T1D Patient Decision Simulator (T1D-PDS) published in Vettoretti et al. Methods: Data of 32 T1D subjects under free-living conditions for 4874 days were used. Univariate probability density function (PDF) parametric models with different candidate shapes were fitted, individually, against sample distributions of: CHO amounts of breakfast (CHOB), lunch (CHOL), dinner (CHOD), and snack (CHOS); breakfast timing (TB); and time between breakfast-lunch (TBL) and between lunch-dinner (TLD). Furthermore, a support vector machine (SVM) classifier was developed to predict the occurrence of a snack in future fixed-length time windows. Once embedded inside the T1D-PDS, an ISCT was performed. Results: Resulting PDF models were: gamma (CHOB, CHOS), lognormal (CHOL, TB), loglogistic (CHOD), and generalized-extreme-values (TBL, TLD). The SVM showed a classification accuracy of 0.8 over the test set. The distributions of simulated meal data were not statistically different from the distributions of the real data used to develop the models (α = 0.05). Conclusions: The models of meal amount and timing variability developed are suitable for describing real data. Their inclusion in modules that describe patient behavior in the T1D-PDS can permit investigators to perform more realistic, reliable, and insightful ISCTs.

Subjects by Vocabulary

Medical Subject Headings: digestive, oral, and skin physiology food and beverages

Microsoft Academic Graph classification: Mathematical model In silico clinical trials Support vector machine Clinical trial Type 1 diabetes medicine.disease medicine Machine learning computer.software_genre computer Parametric modelling Computer science Artificial intelligence business.industry business

Subjects

Original Articles, in-silico clinical trials, maximum absolute difference, parametric modelling, machine learning, support vector machine, in-silico clinical trials; machine learning; maximum absolute difference; parametric modelling; support vector machine, Biomedical Engineering, Bioengineering, Endocrinology, Diabetes and Metabolism, Internal Medicine

Related Organizations
Funded by
EC| Hypo-RESOLVE
Project
Hypo-RESOLVE
Hypoglycaemia - REdefining SOLutions for better liVEs
  • Funder: European Commission (EC)
  • Project Code: 777460
  • Funding stream: H2020 | IMI2-RIA
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