131 related articles for article (PubMed ID: 37195837)
1. Individualized Models for Glucose Prediction in Type 1 Diabetes: Comparing Black-Box Approaches to a Physiological White-Box One.
Cappon G; Prendin F; Facchinetti A; Sparacino G; Favero SD
IEEE Trans Biomed Eng; 2023 Nov; 70(11):3105-3115. PubMed ID: 37195837
[TBL] [Abstract][Full Text] [Related]
2. Assessment of Seasonal Stochastic Local Models for Glucose Prediction without Meal Size Information under Free-Living Conditions.
Prendin F; Díez JL; Del Favero S; Sparacino G; Facchinetti A; Bondia J
Sensors (Basel); 2022 Nov; 22(22):. PubMed ID: 36433278
[TBL] [Abstract][Full Text] [Related]
3. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes.
Woldaregay AZ; Årsand E; Walderhaug S; Albers D; Mamykina L; Botsis T; Hartvigsen G
Artif Intell Med; 2019 Jul; 98():109-134. PubMed ID: 31383477
[TBL] [Abstract][Full Text] [Related]
4. Incorporating Glucose Variability into Glucose Forecasting Accuracy Assessment Using the New Glucose Variability Impact Index and the Prediction Consistency Index: An LSTM Case Example.
Mosquera-Lopez C; Jacobs PG
J Diabetes Sci Technol; 2022 Jan; 16(1):7-18. PubMed ID: 34490793
[TBL] [Abstract][Full Text] [Related]
5. Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal.
Liu C; Vehí J; Avari P; Reddy M; Oliver N; Georgiou P; Herrero P
Sensors (Basel); 2019 Oct; 19(19):. PubMed ID: 31597288
[TBL] [Abstract][Full Text] [Related]
6. Benchmarking Machine Learning Algorithms on Blood Glucose Prediction for Type I Diabetes in Comparison With Classical Time-Series Models.
Xie J; Wang Q
IEEE Trans Biomed Eng; 2020 Nov; 67(11):3101-3124. PubMed ID: 32091990
[TBL] [Abstract][Full Text] [Related]
7. Combined Use of Glucose-Specific Model Identification and Alarm Strategy Based on Prediction-Funnel to Improve Online Forecasting of Hypoglycemic Events.
Faccioli S; Prendin F; Facchinetti A; Sparacino G; Del Favero S
J Diabetes Sci Technol; 2023 Sep; 17(5):1295-1303. PubMed ID: 35611461
[TBL] [Abstract][Full Text] [Related]
8. Linear Model Identification for Personalized Prediction and Control in Diabetes.
Faccioli S; Facchinetti A; Sparacino G; Pillonetto G; Del Favero S
IEEE Trans Biomed Eng; 2022 Feb; 69(2):558-568. PubMed ID: 34347589
[TBL] [Abstract][Full Text] [Related]
9. Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only.
Prendin F; Del Favero S; Vettoretti M; Sparacino G; Facchinetti A
Sensors (Basel); 2021 Feb; 21(5):. PubMed ID: 33673415
[TBL] [Abstract][Full Text] [Related]
10. GluNet: A Deep Learning Framework for Accurate Glucose Forecasting.
Li K; Liu C; Zhu T; Herrero P; Georgiou P
IEEE J Biomed Health Inform; 2020 Feb; 24(2):414-423. PubMed ID: 31369390
[TBL] [Abstract][Full Text] [Related]
11. Experimental evaluation of a recursive model identification technique for type 1 diabetes.
Finan DA; Doyle FJ; Palerm CC; Bevier WC; Zisser HC; Jovanovic L; Seborg DE
J Diabetes Sci Technol; 2009 Sep; 3(5):1192-202. PubMed ID: 20144436
[TBL] [Abstract][Full Text] [Related]
12. Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques.
Zhang Y; Gao G
J Healthc Eng; 2022; 2022():8956850. PubMed ID: 35449869
[TBL] [Abstract][Full Text] [Related]
13. Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction.
Rabby MF; Tu Y; Hossen MI; Lee I; Maida AS; Hei X
BMC Med Inform Decis Mak; 2021 Mar; 21(1):101. PubMed ID: 33726723
[TBL] [Abstract][Full Text] [Related]
14. Long-Term Prediction of Blood Glucose Levels in Type 1 Diabetes Using a CNN-LSTM-Based Deep Neural Network.
Jaloli M; Cescon M
J Diabetes Sci Technol; 2023 Nov; 17(6):1590-1601. PubMed ID: 35466701
[TBL] [Abstract][Full Text] [Related]
15. Prediction of Blood Risk Score in Diabetes Using Deep Neural Networks.
Toledo-Marín JQ; Ali T; van Rooij T; Görges M; Wasserman WW
J Clin Med; 2023 Feb; 12(4):. PubMed ID: 36836230
[TBL] [Abstract][Full Text] [Related]
16. A Stacked Long Short-Term Memory Approach for Predictive Blood Glucose Monitoring in Women with Gestational Diabetes Mellitus.
Lu HY; Lu P; Hirst JE; Mackillop L; Clifton DA
Sensors (Basel); 2023 Sep; 23(18):. PubMed ID: 37766044
[TBL] [Abstract][Full Text] [Related]
17. A personalized multitasking framework for real-time prediction of blood glucose levels in type 1 diabetes patients.
Yang H; Li W; Tian M; Ren Y
Math Biosci Eng; 2024 Jan; 21(2):2515-2541. PubMed ID: 38454694
[TBL] [Abstract][Full Text] [Related]
18. A personalized blood glucose level prediction model with a fine-tuning strategy: A proof-of-concept study.
Seo W; Park SW; Kim N; Jin SM; Park SM
Comput Methods Programs Biomed; 2021 Nov; 211():106424. PubMed ID: 34598081
[TBL] [Abstract][Full Text] [Related]
19. Using an interpretable deep learning model for the prediction of riverine suspended sediment load.
Mohammadi-Raigani Z; Gholami H; Mohamadifar A; Samani AN; Pradhan B
Environ Sci Pollut Res Int; 2024 May; 31(22):32480-32493. PubMed ID: 38656723
[TBL] [Abstract][Full Text] [Related]
20. Using nonlinear auto-regressive with exogenous input neural network (NNARX) in blood glucose prediction.
Allam F
Bioelectron Med; 2024 Apr; 10(1):11. PubMed ID: 38627825
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
