Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models

Abstract In patients with type 1 diabetes mellitus (T1DM), glucose dynamics are influenced by insulin reactions, diet, lifestyle, etc., and characterized by instability and nonlinearity. With the objective of a dependable decision support system for T1DM self-management, we aim to model glucose dyna...

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Autores principales:	Mirela Frandes, Bogdan Timar, Romulus Timar, Diana Lungeanu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b60fa2131072410cbb72e6a125e9f550
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spelling	oai:doaj.org-article:b60fa2131072410cbb72e6a125e9f5502021-12-02T12:31:48ZChaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models10.1038/s41598-017-06478-42045-2322https://doaj.org/article/b60fa2131072410cbb72e6a125e9f5502017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-06478-4https://doaj.org/toc/2045-2322Abstract In patients with type 1 diabetes mellitus (T1DM), glucose dynamics are influenced by insulin reactions, diet, lifestyle, etc., and characterized by instability and nonlinearity. With the objective of a dependable decision support system for T1DM self-management, we aim to model glucose dynamics using their nonlinear chaotic properties. A group of patients was monitored via continuous glucose monitoring (CGM) sensors for several days under free-living conditions. We assessed the glycemic variability (GV) and chaotic properties of each time series. Time series were subsequently transformed into the phase-space and individual autoregressive (AR) models were applied to predict glucose values over 30-minute and 60-minute prediction horizons (PH). The logistic smooth transition AR (LSTAR) model provided the best prediction accuracy for patients with high GV. For a PH of 30 minutes, the average values of root mean squared error (RMSE) and mean absolute error (MAE) for the LSTAR model in the case of patients in the hypoglycemia range were 5.83 ( ± 1.95) mg/dL and 5.18 ( ± 1.64) mg/dL, respectively. For a PH of 60 minutes, the average values of RMSE and MAE were 7.43 ( ± 1.87) mg/dL and 6.54 ( ± 1.6) mg/dL, respectively. Without the burden of measuring exogenous information, nonlinear regime-switching AR models provided fast and accurate results for glucose prediction.Mirela FrandesBogdan TimarRomulus TimarDiana LungeanuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mirela Frandes Bogdan Timar Romulus Timar Diana Lungeanu Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
description	Abstract In patients with type 1 diabetes mellitus (T1DM), glucose dynamics are influenced by insulin reactions, diet, lifestyle, etc., and characterized by instability and nonlinearity. With the objective of a dependable decision support system for T1DM self-management, we aim to model glucose dynamics using their nonlinear chaotic properties. A group of patients was monitored via continuous glucose monitoring (CGM) sensors for several days under free-living conditions. We assessed the glycemic variability (GV) and chaotic properties of each time series. Time series were subsequently transformed into the phase-space and individual autoregressive (AR) models were applied to predict glucose values over 30-minute and 60-minute prediction horizons (PH). The logistic smooth transition AR (LSTAR) model provided the best prediction accuracy for patients with high GV. For a PH of 30 minutes, the average values of root mean squared error (RMSE) and mean absolute error (MAE) for the LSTAR model in the case of patients in the hypoglycemia range were 5.83 ( ± 1.95) mg/dL and 5.18 ( ± 1.64) mg/dL, respectively. For a PH of 60 minutes, the average values of RMSE and MAE were 7.43 ( ± 1.87) mg/dL and 6.54 ( ± 1.6) mg/dL, respectively. Without the burden of measuring exogenous information, nonlinear regime-switching AR models provided fast and accurate results for glucose prediction.
format	article
author	Mirela Frandes Bogdan Timar Romulus Timar Diana Lungeanu
author_facet	Mirela Frandes Bogdan Timar Romulus Timar Diana Lungeanu
author_sort	Mirela Frandes
title	Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
title_short	Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
title_full	Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
title_fullStr	Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
title_full_unstemmed	Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
title_sort	chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/b60fa2131072410cbb72e6a125e9f550
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Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models

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