nach oben

Arabian Journal for Science and Engineering

Erschienen in:

05.01.2021 | Research Article-Electrical Engineering

Personalized Advanced Time Blood Glucose Level Prediction

verfasst von: Asiye Şahin, Ahmet Aydın

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 10/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The introduction of continuous glucose monitoring (CGM) devices for glucose level measurement accelerated the application of artificial intelligence methods in predicting advanced time blood glucose levels by providing lots of continuous structured data needed to train the methods. Advanced time blood glucose level prediction enables diabetic patients to better manage their blood glucose levels and receive early warnings about the wrong treatments and adverse conditions such as hypoglycemia or hyperglycemia. In this study, an artificial neural network is trained for 30- and 60-min prediction horizon by using physiological models for insulin injection, carbohydrate intake, and physical activity in addition to past CGM data for each of six real T1D patients. The mean of the prediction error for six patients is obtained as 18.81 mg/dL and 30.89 mg/dL for 30- and 60-min prediction horizons, respectively. These results are better than the other studies in the literature that use real patient data, and the model is computationally simpler compared to the deep learning-based methods. Therefore, in this study, a model that can be implemented on the mobile or embedded device, learn the patient’s physiologic dynamics, and make accurate predictions during the measurements is developed and presented.

Vorheriger Artikel Power Transmission Line Fault Detection and Diagnosis Based on Artificial Intelligence Approach and its Development in UAV: A Review

Nächster Artikel Distance Estimation-Based PSO Between Patient with Alzheimer’s Disease and Beacon Node in Wireless Sensor Networks

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Saeedi, P.; et al.: Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract. 157, 107843 (2019). https://doi.org/10.1016/j.diabres.2019.107843CrossRef

Contreras, I.; Vehi, J.: Artificial intelligence for diabetes management and decision support: literature review. J. Med. Internet Res. 20(5), 1–21 (2018). https://doi.org/10.2196/10775CrossRef

Abualigah, L.M.Q.: Feature Selection and Enhanced Krill Herd Algorithm for Text Document Clustering, vol. 816. Springer, Cham (2019)CrossRef

Abualigah, L.: Multi-verse optimizer algorithm: a comprehensive survey of its results, variants, and applications. Neural Comput. Appl. 32(16), 12381–12401 (2020). https://doi.org/10.1007/s00521-020-04839-1CrossRef

Abualigah, L.M.; Khader, A.T.; Hanandeh, E.S.: Hybrid clustering analysis using improved krill herd algorithm. Appl. Intell. 48(11), 4047–4071 (2018). https://doi.org/10.1007/s10489-018-1190-6CrossRef

Abualigah, L.; Diabat, A.: A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments. Cluster Comput. (2020). https://doi.org/10.1007/s10586-020-03075-5CrossRef

Abualigah, L.: Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications. Neural Comput. Appl. (2020). https://doi.org/10.1007/s00521-020-05107-yCrossRef

Zanderigo, F.; Sparacino, G.; Kovatchev, B.; Cobelli, C.: Glucose prediction algorithms from continuous monitoring data: assessment of accuracy via continuous glucose error-grid analysis. J. Diabetes Sci. Technol. 1(5), 645–651 (2007). https://doi.org/10.1177/193229680700100508CrossRef

Reifman, J.; Rajaraman, S.; Gribok, A.; Ward, W.K.: Predictive monitoring for improved management of glucose levels. J. Diabetes Sci. Technol. 1(4), 478–486 (2007). https://doi.org/10.1177/193229680700100405CrossRef

10.

Eren-oruklu, M.; Cinar, A.; Ph, D.; Quinn, L.; Ph, D.; Smith, D.: Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol. Ther. 11(4), 243–253 (2009)CrossRef

11.

Zhang, Y.; Kang, R.; Xiang, S.: Research on glucose concentration predicting based on ARMA model. In: Proc. 2014 Progn. Syst. Heal. Manag. Conf. PHM 2014, pp. 332–335 (2014). https://doi.org/10.1109/phm.2014.6988189.

12.

Contreras, I.; Oviedo, S.; Vettoretti, M.; Visentin, R.; Vehí, J.: Personalized blood glucose prediction: a hybrid approach using grammatical evolution and physiological models. PLoS ONE 12(11), 1–16 (2017). https://doi.org/10.1371/journal.pone.0187754CrossRef

13.

Contreras, I.; Bertachi, A.; Biagi, L.; Oviedo, S.; Vehí, J.: Using grammatical evolution to generate short-term blood glucose prediction models. CEUR Workshop Proc. 2148, 91–96 (2018)

14.

Reymann, M.P.; Dorschky, E.; Groh, B.H.; Martindale, C.; Blank, P.; Eskofier, B.M.: Blood glucose level prediction based on support vector regression using mobile platforms. In: Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2016-Octob, pp. 2990–2993 (2016). https://doi.org/10.1109/embc.2016.7591358.

15.

Georga, E.I.; et al.: Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE J. Biomed. Heal. Inf. 17(1), 71–81 (2013). https://doi.org/10.1109/TITB.2012.2219876CrossRef

16.

Georga, E.I.; Protopappas, V.C.; Polyzos, D.; Fotiadis, D.I.: A predictive model of subcutaneous glucose concentration in type 1 diabetes based on Random Forests. In: Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, pp. 2889–2892 (2012). https://doi.org/10.1109/embc.2012.6346567

17.

Midroni, C.; Leimbigler, P.J.; Baruah, G.; Kolla, M.; Whitehead, A.J.; Fossat, Y.: Predicting glycemia in type 1 diabetes patients: experiments with XGBoost. CEUR Workshop Proc. 2148, 79–84 (2018)

18.

Wang, Y.; Wu, X.; Mo, X.: A novel adaptive-weighted-average framework for blood glucose prediction. Diabetes Technol. Ther. 15(10), 792–801 (2013). https://doi.org/10.1089/dia.2013.0104CrossRef

19.

Allam, F.; Nossai, Z.; Gomma, H.; Ibrahim, I.; Abdelsalam, M.: A recurrent neural network approach for predicting glucose concentration in type-1 diabetic patients. IFIP Adv. Inf. Commun. Technol. 363(1), 254–259 (2011). https://doi.org/10.1007/978-3-642-23957-1_29CrossRef

20.

Martinsson, J.; Schliep, A.; Eliasson, B.; Meijner, C.; Persson, S.; Mogren, O.: Automatic blood glucose prediction with confidence using recurrent neural networks. CEUR Workshop Proc. 2148, 64–68 (2018)

21.

Meijner, C.; Persson, S.: Blood Glucose Prediction for Type 1 Diabetes using Machine Learning, pp. 1–77 (2017)

22.

Mirshekarian, S.: Blood Glucose Level Prediction via Seamless Incorporation of Raw Features Using RNNs (2018)

23.

Aiello, E.M.; Lisanti, G.; Magni, L.; Musci, M.; Toffanin, C.: Therapy-driven deep glucose forecasting. Eng. Appl. Artif. Intell. 87, 103255 (2019). https://doi.org/10.1016/j.engappai.2019.103255CrossRef

24.

Sun, Q.; Jankovic, M.V.; Bally, L.; Mougiakakou, S.G.: Predicting blood glucose with an LSTM and Bi-LSTM based deep neural network. In: 2018 14th Symp. Neural Networks Appl. NEUREL 2018 (2018). https://doi.org/10.1109/neurel.2018.8586990

26.

Li, K.; Liu, C.; Zhu, T.; Herrero, P.; Georgiou, P.: GluNet: a deep learning framework for accurate glucose forecasting. IEEE J. Biomed. Heal. Inf. 24(2), 414–423 (2020)CrossRef

27.

Mhaskar, H.N.; Pereverzyev, S.V.; van der Walt, M.D.: A deep learning approach to diabetic blood glucose prediction. Front. Appl. Math. Stat. 3(July), 1–11 (2017). https://doi.org/10.3389/fams.2017.00014CrossRef

28.

Bertachi, A.; Biagi, L.; Contreras, I.; Luo, N.; Vehí, J.: Prediction of blood glucose levels and nocturnal hypoglycemia using physiological models and artificial neural networks. CEUR Workshop Proc. 2148, 85–90 (2018)

29.

Daskalaki, E.; Prountzou, A.; Diem, P.; Mougiakakou, S.G.: Real-Time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients. Diabetes Technol. Ther. 14(2), 168–174 (2012). https://doi.org/10.1089/dia.2011.0093CrossRef

30.

Pappada, S.M.; et al.: Development of a neural network model for predicting glucose levels in a surgical critical care setting. Patient Saf. Surg. 4(1), 15 (2010). https://doi.org/10.1186/1754-9493-4-15CrossRef

31.

Pappada, S.M.; et al.: Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol. Ther. 13(2), 135–141 (2011). https://doi.org/10.1089/dia.2010.0104CrossRef

32.

Zecchin, C.; Facchinetti, A.; Sparacino, G.; De Nicolao, G.; Cobelli, C.: Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration. IEEE Trans. Biomed. Eng. 59(6), 1550–1560 (2012). https://doi.org/10.1109/TBME.2012.2188893CrossRef

33.

Marling, C.; Bunescu, R.: The OhioT1DM dataset for blood glucose level prediction. CEUR Workshop Proc. 2148, 60–63 (2018)

34.

Lee, H.; Buckingham, B.A.; Wilson, D.M.; Bequette, B.W.: A closed-loop artificial pancreas using model predictive control and a sliding meal size estimator. J. Diabetes Sci. Technol. 3(5), 1082–1090 (2009). https://doi.org/10.1177/193229680900300511CrossRef

35.

Hovorka, R.; et al.: Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Meas. 25(4), 905–920 (2004). https://doi.org/10.1088/0967-3334/25/4/010CrossRef

Titel: Personalized Advanced Time Blood Glucose Level Prediction
verfasst von: Asiye Şahin
Ahmet Aydın
Publikationsdatum: 05.01.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 10/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-020-05263-2

Premium Partner

Marktübersichten

Die im Laufe eines Jahres in der „adhäsion“ veröffentlichten Marktübersichten helfen Anwendern verschiedenster Branchen, sich einen gezielten Überblick über Lieferantenangebote zu verschaffen.

Zur Marktübersicht

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 10/2021

Advanced Relaying for DG-Penetrated Distribution System

Recurrent Quantification Analysis-Based Emotion Classification in Stroke Using Electroencephalogram Signals

A 2 mA PA Driver Capable of Handling 1μF Load Capacitance with a Three-Stage Class-AB Rail-To-Rail Amplifier

Cognitive Control Using Adaptive RBF Neural Networks and Reinforcement Learning for Networked Control System Subject to Time-Varying Delay and Packet Losses

Observer-Based Leader Follower Network Multi-Motor Drive: An Agent-Based Coordination Control Approach

Hybrid Multivariate Statistical and Neural Network Model to Predict Greenhouse Gas Emissions

Premium Partner

Marktübersichten