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 2023;63(7):902 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
  12. Kuang Y, Liu Y, Pei Q, Ning X, Zou Y, Liu L, Song L, Guo C, Sun Y, Deng K, Zou C, Cao D, Cui Y, Wu C, Yang G. Long Short-Term Memory Network for Development and Simulation of Warfarin Dosing Model Based on Time Series Anticoagulant Data. Frontiers in Cardiovascular Medicine 2022;9 View
  13. Nagpal M, Barbaric A, Sherifali D, Morita P, Cafazzo J. Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review. JMIR Diabetes 2021;6(4):e29027 View
  14. Fowers R, Berardi V, Huberty J, Stecher C. Using mobile meditation app data to predict future app engagement: an observational study. Journal of the American Medical Informatics Association 2022;29(12):2057 View
  15. Alhaddad A, Aly H, Gad H, Al-Ali A, Sadasivuni K, Cabibihan J, Malik R. Sense and Learn: Recent Advances in Wearable Sensing and Machine Learning for Blood Glucose Monitoring and Trend-Detection. Frontiers in Bioengineering and Biotechnology 2022;10 View
  16. Shahzad Y, Javed H, Farman H, Ahmad J, Jan B, A. Nassani A. Optimized Predictive Framework for Healthcare Through Deep Learning. Computers, Materials & Continua 2021;67(2):2463 View
  17. Lee S, Chu Y, Ryu J, Park Y, Yang S, Koh S. Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis. Yonsei Medical Journal 2022;63(Suppl):S93 View
  18. Deng Y, Chang H, Li H. Recent Advances in Computational Modeling of Biomechanics and Biorheology of Red Blood Cells in Diabetes. Biomimetics 2022;7(1):15 View
  19. Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi M. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetology & Metabolic Syndrome 2022;14(1) View
  20. Triantafyllidis A, Kondylakis H, Katehakis D, Kouroubali A, Koumakis L, Marias K, Alexiadis A, Votis K, Tzovaras D. Deep Learning in mHealth for Cardiovascular Disease, Diabetes, and Cancer: Systematic Review. JMIR mHealth and uHealth 2022;10(4):e32344 View
  21. Kim H, Hwang S, Lee S, Kim Y. Classification and Prediction on Hypertension with Blood Pressure Determinants in a Deep Learning Algorithm. International Journal of Environmental Research and Public Health 2022;19(22):15301 View
  22. Shaikh F, Haworth N, Wells R, Bishop J, Chatterjee S, Banerjee S, Laha S. Compact Instrumentation for Accurate Detection and Measurement of Glucose Concentration Using Photoacoustic Spectroscopy. IEEE Access 2022;10:31885 View
  23. Stecher C, Berardi V, Fowers R, Christ J, Chung Y, Huberty J. Identifying App-Based Meditation Habits and the Associated Mental Health Benefits: Longitudinal Observational Study. Journal of Medical Internet Research 2021;23(11):e27282 View
  24. Ratzki-Leewing A, Ryan B, Zou G, Webster-Bogaert S, Black J, Stirling K, Timcevska K, Khan N, Buchenberger J, Harris S. Predicting Real-world Hypoglycemia Risk in American Adults With Type 1 or 2 Diabetes Mellitus Prescribed Insulin and/or Secretagogues: Protocol for a Prospective, 12-Wave Internet-Based Panel Survey With Email Support (the iNPHORM [Investigating Novel Predictions of Hypoglycemia Occurrence Using Real-world Models] Study). JMIR Research Protocols 2022;11(2):e33726 View
  25. Valero M, Pola P, Falaiye O, Ingram K, Zhao L, Shahriar H, Ahamed S. Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study. JMIR Formative Research 2022;6(8):e38664 View
  26. Yadav V, Nilam . Comparison of machine learning techniques for precision in measurement of glucose level in artificial pancreas. Mathematical Methods in the Applied Sciences 2023 View

Books/Policy Documents

  1. Malhotra S, Chhikara R. Intelligent Healthcare. View
  2. Li S, Wang J. Diabetes Digital Health and Telehealth. View