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

التفاصيل البيبلوغرافية
العنوان: Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models
المؤلفون: Mirela Frandes, Bogdan Timar, Romulus Timar, Diana Lungeanu
المصدر: Scientific Reports, Vol 7, Iss 1, Pp 1-10 (2017)
بيانات النشر: Nature Portfolio, 2017.
سنة النشر: 2017
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2045-2322
Relation: https://doaj.org/toc/2045-2322
DOI: 10.1038/s41598-017-06478-4
URL الوصول: https://doaj.org/article/b60fa2131072410cbb72e6a125e9f550
رقم الأكسشن: edsdoj.b60fa2131072410cbb72e6a125e9f550
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20452322
DOI:10.1038/s41598-017-06478-4