Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14452, first published .
Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial

Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial

Development of a Deep Learning Model for Dynamic Forecasting of Blood Glucose Level for Type 2 Diabetes Mellitus: Secondary Analysis of a Randomized Controlled Trial

Journals

  1. Hatmal M, Abderrahman S, Nimer W, Al-Eisawi Z, Al-Ameer H, Al-Hatamleh M, Mohamud R, Alshaer W. Artificial Neural Networks Model for Predicting Type 2 Diabetes Mellitus Based on VDR Gene FokI Polymorphism, Lipid Profile and Demographic Data. Biology 2020;9(8):222 View
  2. Timpel P, Oswald S, Schwarz P, Harst L. Mapping the Evidence on the Effectiveness of Telemedicine Interventions in Diabetes, Dyslipidemia, and Hypertension: An Umbrella Review of Systematic Reviews and Meta-Analyses. Journal of Medical Internet Research 2020;22(3):e16791 View
  3. Faruqui S, Alaeddini A, Chang M, Shirinkam S, Jaramillo C, NajafiRad P, Wang J, Pugh M. Summarizing Complex Graphical Models of Multiple Chronic Conditions Using the Second Eigenvalue of Graph Laplacian: Algorithm Development and Validation. JMIR Medical Informatics 2020;8(6):e16372 View
  4. Owais M, Arsalan M, Mahmood T, Kang J, Park K. Automated Diagnosis of Various Gastrointestinal Lesions Using a Deep Learning–Based Classification and Retrieval Framework With a Large Endoscopic Database: Model Development and Validation. Journal of Medical Internet Research 2020;22(11):e18563 View
  5. Kim H, Lim D, Kim Y. Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey. International Journal of Environmental Research and Public Health 2021;18(11):5597 View
  6. Ramazi R, Perndorfer C, Soriano E, Laurenceau J, Beheshti R. Predicting progression patterns of type 2 diabetes using multi-sensor measurements. Smart Health 2021;21:100206 View
  7. van Doorn W, Foreman Y, Schaper N, Savelberg H, Koster A, van der Kallen C, Wesselius A, Schram M, Henry R, Dagnelie P, de Galan B, Bekers O, Stehouwer C, Meex S, Brouwers M, Chen C. Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study. PLOS ONE 2021;16(6):e0253125 View
  8. Zhang M, Flores K, Tran H. Deep learning and regression approaches to forecasting blood glucose levels for type 1 diabetes. Biomedical Signal Processing and Control 2021;69:102923 View
  9. Deng Y, Lu L, Aponte L, Angelidi A, Novak V, Karniadakis G, Mantzoros C. Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. npj Digital Medicine 2021;4(1) View
  10. Oliveira Chaves L, Gomes Domingos A, Louzada Fernandes D, Ribeiro Cerqueira F, Siqueira-Batista R, Bressan J. Applicability of machine learning techniques in food intake assessment: A systematic review. Critical Reviews in Food Science and Nutrition 2021:1 View
  11. Zhu T, Li K, Herrero P, Georgiou P. Deep Learning for Diabetes: A Systematic Review. IEEE Journal of Biomedical and Health Informatics 2021;25(7):2744 View

Books/Policy Documents

  1. Malhotra S, Chhikara R. Intelligent Healthcare. View