This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1328) Reviews on Diabetes Technologies and Innovations (20) Diabetes Health Services and System Innovations (104)

Published on in Vol 20, No 5 (2018): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10775, first published .
Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Authors of this article:

Ivan Contreras1 Author Orcid Image ;   Josep Vehi1, 2 Author Orcid Image
Article Authors Cited by (283) Tweetations (63) Metrics

Journals

  1. Lunenfeld B, Bilger W, Longobardi S, Kirsten J, D’Hooghe T, Sunkara S. Decision points for individualized hormonal stimulation with recombinant gonadotropins for treatment of women with infertility. Gynecological Endocrinology 2019;35(12):1027 View
  2. Kukafka R. Digital Health Consumers on the Road to the Future. Journal of Medical Internet Research 2019;21(11):e16359 View
  3. Agrawal A, Gans J, Goldfarb A. Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. Journal of Economic Perspectives 2019;33(2):31 View
  4. Shokrekhodaei M, Quinones S. Review of Non-Invasive Glucose Sensing Techniques: Optical, Electrical and Breath Acetone. Sensors 2020;20(5):1251 View
  5. Tejedor M, Woldaregay A, Godtliebsen F. Reinforcement learning application in diabetes blood glucose control: A systematic review. Artificial Intelligence in Medicine 2020;104:101836 View
  6. Preethi P, Asokan R. Modelling LSUTE: PKE Schemes for Safeguarding Electronic Healthcare Records Over Cloud Communication Environment. Wireless Personal Communications 2021;117(4):2695 View
  7. Ambagtsheer R, Shafiabady N, Dent E, Seiboth C, Beilby J. The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. International Journal of Medical Informatics 2020;136:104094 View
  8. Bertachi A, Viñals C, Biagi L, Contreras I, Vehí J, Conget I, Giménez M. Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor. Sensors 2020;20(6):1705 View
  9. Abd-Alrazaq A, Alajlani M, Alhuwail D, Schneider J, Al-Kuwari S, Shah Z, Hamdi M, Househ M. Artificial Intelligence in the Fight Against COVID-19: Scoping Review. Journal of Medical Internet Research 2020;22(12):e20756 View
  10. Agrawal A, Gans J, Goldfarb A. Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction. SSRN Electronic Journal 2019 View
  11. Broome D, Hilton C, Mehta N. Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management. Current Diabetes Reports 2020;20(2) View
  12. Sosunkevic S, Rapalis A, Marozas M, Ceponis J, Lukosevicius A. Diabetic Vascular Damage: Review of Pathogenesis and Possible Evaluation Technologies. IEEE Access 2019;7:148511 View
  13. Taylor K, Forlenza G. Use of Machine Learning and Hybrid Closed Loop Insulin Delivery at Diabetes Camps. Diabetes Technology & Therapeutics 2020;22(7):535 View
  14. Asgari S, Scalzo F, Kasprowicz M. Pattern Recognition in Medical Decision Support. BioMed Research International 2019;2019:1 View
  15. Li J, Liang J, Laken S, Langer R, Traverso G. Clinical Opportunities for Continuous Biosensing and Closed-Loop Therapies. Trends in Chemistry 2020;2(4):319 View
  16. Oviedo S, Contreras I, Bertachi A, Quirós C, Giménez M, Conget I, Vehi J. Minimizing postprandial hypoglycemia in Type 1 diabetes patients using multiple insulin injections and capillary blood glucose self-monitoring with machine learning techniques. Computer Methods and Programs in Biomedicine 2019;178:175 View
  17. Channa R, Wolf R, Abramoff M. Autonomous Artificial Intelligence in Diabetic Retinopathy: From Algorithm to Clinical Application. Journal of Diabetes Science and Technology 2021;15(3):695 View
  18. Massaro A, Maritati V, Giannone D, Convertini D, Galiano A. LSTM DSS Automatism and Dataset Optimization for Diabetes Prediction. Applied Sciences 2019;9(17):3532 View
  19. Ljubic B, Hai A, Stanojevic M, Diaz W, Polimac D, Pavlovski M, Obradovic Z. Predicting complications of diabetes mellitus using advanced machine learning algorithms. Journal of the American Medical Informatics Association 2020;27(9):1343 View
  20. Ellahham S. Artificial Intelligence: The Future for Diabetes Care. The American Journal of Medicine 2020;133(8):895 View
  21. Nagaraj S, Sidorenkov G, van Boven J, Denig P. Predicting short‐ and long‐term glycated haemoglobin response after insulin initiation in patients with type 2 diabetes mellitus using machine‐learning algorithms. Diabetes, Obesity and Metabolism 2019;21(12):2704 View
  22. Hosseini M, Zargoush M, Alemi F, Kheirbek R. Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management. Journal of Big Data 2020;7(1) View
  23. Rajšp A, Fister I. A Systematic Literature Review of Intelligent Data Analysis Methods for Smart Sport Training. Applied Sciences 2020;10(9):3013 View
  24. Woldaregay A, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes. Journal of Medical Internet Research 2019;21(5):e11030 View
  25. Kerr D, Klonoff D. Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris. Journal of Diabetes Science and Technology 2019;13(1):123 View
  26. Triantafyllidis A, Tsanas A. Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature. Journal of Medical Internet Research 2019;21(4):e12286 View
  27. Kim Y, Kelley B, Nasser J, Chung K. Implementing Precision Medicine and Artificial Intelligence in Plastic Surgery: Concepts and Future Prospects. Plastic and Reconstructive Surgery - Global Open 2019;7(3):e2113 View
  28. Silva K, Lee W, Forbes A, Demmer R, Barton C, Enticott J. Use and performance of machine learning models for type 2 diabetes prediction in community settings: A systematic review and meta-analysis. International Journal of Medical Informatics 2020;143:104268 View
  29. Reddy S, Fox J, Purohit M. Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine 2019;112(1):22 View
  30. Vehí J, Contreras I, Oviedo S, Biagi L, Bertachi A. Prediction and prevention of hypoglycaemic events in type-1 diabetic patients using machine learning. Health Informatics Journal 2020;26(1):703 View
  31. Wang J, Warnecke J, Haghi M, Deserno T. Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle. Sensors 2020;20(9):2442 View
  32. Leung R. Increasing the Impact of JMIR Journals in the Attention Economy. Journal of Medical Internet Research 2019;21(10):e16172 View
  33. Vettoretti M, Cappon G, Facchinetti A, Sparacino G. Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors. Sensors 2020;20(14):3870 View
  34. Ibrahim M, Baker J, Cahn A, Eckel R, El Sayed N, Fischl A, Gaede P, Leslie R, Pieralice S, Tuccinardi D, Pozzilli P, Richelsen B, Roitman E, Standl E, Toledano Y, Tuomilehto J, Weber S, Umpierrez G. Hypoglycaemia and its management in primary care setting. Diabetes/Metabolism Research and Reviews 2020;36(8) View
  35. Khodaei M, Candelino N, Mehrvarz A, Jalili N. Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation. IEEE Access 2020;8:23965 View
  36. Bruno A, Johnston K, Durkalski-Mauldin V. Treatment of Hyperglycemia in Patients With Acute Stroke—Reply. JAMA 2019;322(22):2248 View
  37. Li J, Huang J, Zheng L, Li X. Application of Artificial Intelligence in Diabetes Education and Management: Present Status and Promising Prospect. Frontiers in Public Health 2020;8 View
  38. Musacchio N, Giancaterini A, Guaita G, Ozzello A, Pellegrini M, Ponzani P, Russo G, Zilich R, de Micheli A. Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists. Journal of Medical Internet Research 2020;22(6):e16922 View
  39. Elhadd T, Mall R, Bashir M, Palotti J, Fernandez-Luque L, Farooq F, Mohanadi D, Dabbous Z, Malik R, Abou-Samra A. Artificial Intelligence (AI) based machine learning models predict glucose variability and hypoglycaemia risk in patients with type 2 diabetes on a multiple drug regimen who fast during ramadan (The PROFAST – IT Ramadan study). Diabetes Research and Clinical Practice 2020;169:108388 View
  40. Martinez-Millana A, Jarones E, Fernandez-Llatas C, Hartvigsen G, Traver V. App Features for Type 1 Diabetes Support and Patient Empowerment: Systematic Literature Review and Benchmark Comparison. JMIR mHealth and uHealth 2018;6(11):e12237 View
  41. Javaid M, Haleem A, Khan I, Vaishya R, Vaish A. Extending capabilities of artificial intelligence for decision-making and healthcare education. Apollo Medicine 2020;17(1):53 View
  42. Mishra D, Shukla S. ROLE OF ARTIFICIAL INTELLIGENCE IN DIABETES MANAGEMENT. International Journal of Engineering Technologies and Management Research 2020;7(7):80 View
  43. Zhu T, Li K, Kuang L, Herrero P, Georgiou P. An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning. Sensors 2020;20(18):5058 View
  44. Forlenza G. Use of Artificial Intelligence to Improve Diabetes Outcomes in Patients Using Multiple Daily Injections Therapy. Diabetes Technology & Therapeutics 2019;21(S2):S2-4 View
  45. Abhari S, Niakan Kalhori S, Ebrahimi M, Hasannejadasl H, Garavand A. Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods. Healthcare Informatics Research 2019;25(4):248 View
  46. Shen J, Chen J, Zheng Z, Zheng J, Liu Z, Song J, Wong S, Wang X, Huang M, Fang P, Jiang B, Tsang W, He Z, Liu T, Akinwunmi B, Wang C, Zhang C, Huang J, Ming W. An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research 2020;22(9):e21573 View
  47. Stechova K, Hlubik J, Pithova P, Cikl P, Lhotska L. Comprehensive Analysis of the Real Lifestyles of T1D Patients for the Purpose of Designing a Personalized Counselor for Prandial Insulin Dosing. Nutrients 2019;11(5):1148 View
  48. Guemes A, Cappon G, Hernandez B, Reddy M, Oliver N, Georgiou P, Herrero P. Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers. IEEE Journal of Biomedical and Health Informatics 2020;24(5):1439 View
  49. Carson J, Chakshu N, Sazonov I, Nithiarasu P. Artificial intelligence approaches to predict coronary stenosis severity using non-invasive fractional flow reserve. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2020;234(11):1337 View
  50. Suter-Crazzolara C. Better Patient Outcomes Through Mining of Biomedical Big Data. Frontiers in ICT 2018;5 View
  51. Mujahid O, Contreras I, Vehi J. Machine Learning Techniques for Hypoglycemia Prediction: Trends and Challenges. Sensors 2021;21(2):546 View
  52. Fujihara K, Matsubayashi Y, Harada Yamada M, Yamamoto M, Iizuka T, Miyamura K, Hasegawa Y, Maegawa H, Kodama S, Yamazaki T, Sone H. Machine Learning Approach to Decision Making for Insulin Initiation in Japanese Patients With Type 2 Diabetes (JDDM 58): Model Development and Validation Study. JMIR Medical Informatics 2021;9(1):e22148 View
  53. Li Y, Cao H, Allen C, Wang X, Erdelez S, Shyu C. Computational modeling of human reasoning processes for interpretable visual knowledge: a case study with radiographers. Scientific Reports 2020;10(1) View
  54. Roos T, Hochstadt S, Keuthage W, Kröger J, Lueg A, Mühlen H, Schütte L, Scheper N, Ehrmann D, Hermanns N, Heinemann L, Kulzer B. Level of Digitalization in Germany: Results of the Diabetes Digitalization and Technology (D.U.T) Report 2020. Journal of Diabetes Science and Technology 2022;16(1):144 View
  55. Wang W, Pei X, Zhang L, Chen Z, Lin D, Duan X, Fan J, Pan Q, Guo L. Application of new international classification of adult‐onset diabetes in Chinese inpatients with diabetes mellitus. Diabetes/Metabolism Research and Reviews 2021;37(7) View
  56. Wang Z, Wang J, Kahkoska A, Buse J, Gu Z. Developing Insulin Delivery Devices with Glucose Responsiveness. Trends in Pharmacological Sciences 2021;42(1):31 View
  57. van der Waa J, Nieuwburg E, Cremers A, Neerincx M. Evaluating XAI: A comparison of rule-based and example-based explanations. Artificial Intelligence 2021;291:103404 View
  58. Borle N, Ryan E, Greiner R. The challenge of predicting blood glucose concentration changes in patients with type I diabetes. Health Informatics Journal 2021;27(1):146045822097758 View
  59. Aggarwal N, Ahmed M, Basu S, Curtin J, Evans B, Matheny M, Nundy S, Sendak M, Shachar C, Shah R, Thadaney-Israni S. Advancing Artificial Intelligence in Health Settings Outside the Hospital and Clinic. NAM Perspectives 2020 View
  60. Şahin A, Aydın A. Personalized Advanced Time Blood Glucose Level Prediction. Arabian Journal for Science and Engineering 2021;46(10):9333 View
  61. Kalaiselvan V, Sharma A, Gupta S. “Feasibility test and application of AI in healthcare”—with special emphasis in clinical, pharmacovigilance, and regulatory practices. Health and Technology 2021;11(1):1 View
  62. Zhang Y, Yu H, Dong R, Ji X, Li F, Jiang L. Application Prospect of Artificial Intelligence in Rehabilitation and Management of Myasthenia Gravis. BioMed Research International 2021;2021:1 View
  63. Khanam J, Foo S. A comparison of machine learning algorithms for diabetes prediction. ICT Express 2021;7(4):432 View
  64. Pham Q, Gamble A, Hearn J, Cafazzo J. The Need for Ethnoracial Equity in Artificial Intelligence for Diabetes Management: Review and Recommendations. Journal of Medical Internet Research 2021;23(2):e22320 View
  65. Abokhzam A, Gupta N, Bose D. Efficient diabetes mellitus prediction with grid based random forest classifier in association with natural language processing. International Journal of Speech Technology 2021;24(3):601 View
  66. Contreras I, Calm R, Sainz M, Herrero P, Vehi J. Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty. Mathematics 2021;9(6):631 View
  67. Mouchabac S, Leray P, Adrien V, Gollier-Briant F, Bonnot O. Prevention of Suicidal Relapses in Adolescents With a Smartphone Application: Bayesian Network Analysis of a Preclinical Trial Using In Silico Patient Simulations. Journal of Medical Internet Research 2021;23(9):e24560 View
  68. Rodriguez-León C, Villalonga C, Munoz-Torres M, Ruiz J, Banos O. Mobile and Wearable Technology for the Monitoring of Diabetes-Related Parameters: Systematic Review. JMIR mHealth and uHealth 2021;9(6):e25138 View
  69. Dave D, Erraguntla M, Lawley M, DeSalvo D, Haridas B, McKay S, Koh C. Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study. JMIR Diabetes 2021;6(2):e26909 View
  70. Kim K, Yang H, Yi J, Son H, Ryu J, Kim Y, Jeong J, Chin H, Na K, Chae D, Han S, Kim S. Real-Time Clinical Decision Support Based on Recurrent Neural Networks for In-Hospital Acute Kidney Injury: External Validation and Model Interpretation. Journal of Medical Internet Research 2021;23(4):e24120 View
  71. Yin J, Ngiam K, Teo H. Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of Medical Internet Research 2021;23(4):e25759 View
  72. Douer N, Meyer J. Theoretical, Measured, and Subjective Responsibility in Aided Decision Making. ACM Transactions on Interactive Intelligent Systems 2021;11(1):1 View
  73. Meneghetti L, Dassau E, Doyle F, Del Favero S. Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures. Journal of Diabetes Science and Technology 2022;16(3):641 View
  74. Roski J, Maier E, Vigilante K, Kane E, Matheny M. Enhancing trust in AI through industry self-governance. Journal of the American Medical Informatics Association 2021;28(7):1582 View
  75. Niu X, Chi J, Guo J, Ruan H, Zhang J, Tao H, Wang Y. Effects of nurse-led web-based interventions on people with type 2 diabetes mellitus: A systematic review and meta-analysis. Journal of Telemedicine and Telecare 2021;27(5):269 View
  76. Tarumi S, Takeuchi W, Chalkidis G, Rodriguez-Loya S, Kuwata J, Flynn M, Turner K, Sakaguchi F, Weir C, Kramer H, Shields D, Warner P, Kukhareva P, Ban H, Kawamoto K. Leveraging Artificial Intelligence to Improve Chronic Disease Care: Methods and Application to Pharmacotherapy Decision Support for Type-2 Diabetes Mellitus. Methods of Information in Medicine 2021;60(S 01):e32 View
  77. Biagi L, Bertachi A, Giménez M, Conget I, Bondia J, Martín-Fernández J, Vehí J. Probabilistic Model of Transition between Categories of Glucose Profiles in Patients with Type 1 Diabetes Using a Compositional Data Analysis Approach. Sensors 2021;21(11):3593 View
  78. Fan Y, Long E, Cai L, Cao Q, Wu X, Tong R. Machine Learning Approaches to Predict Risks of Diabetic Complications and Poor Glycemic Control in Nonadherent Type 2 Diabetes. Frontiers in Pharmacology 2021;12 View
  79. Enriquez J, Chu Y, Pudakalakatti S, Hsieh K, Salmon D, Dutta P, Millward N, Lurie E, Millward S, McAllister F, Maitra A, Sen S, Killary A, Zhang J, Jiang X, Bhattacharya P, Shams S. Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer. JMIR Medical Informatics 2021;9(6):e26601 View
  80. Liu C, Tang S, An K, Zhang S, Zhou Y, Su N, Yang R, Liao X, An Z, Li S. Knowledge, Attitude, and Practice of Metformin Extended-Release Tablets Among Clinicians in China: A Cross-Sectional Survey. Frontiers in Pharmacology 2021;12 View
  81. Klimontov V, Berikov V, Saik O. Artificial intelligence in diabetology. Diabetes mellitus 2021;24(2):156 View
  82. Nguyen M, Jankovic I, Kalesinskas L, Baiocchi M, Chen J. Machine learning for initial insulin estimation in hospitalized patients. Journal of the American Medical Informatics Association 2021;28(10):2212 View
  83. 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
  84. Rhee S, Sung J, Kim S, Cho I, Lee S, Chang H. Development and Validation of a Deep Learning Based Diabetes Prediction System Using a Nationwide Population-Based Cohort. Diabetes & Metabolism Journal 2021;45(4):515 View
  85. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  86. Alloatti F, Bosca A, Di Caro L, Pieraccini F. Diabetes and conversational agents: the AIDA project case study. Discover Artificial Intelligence 2021;1(1) View
  87. Khurana A, Leffler D, Gomez K, Thukral C. Short and long-term follow-up and clinical outcomes in patients with celiac disease in a large private practice setting. BMC Gastroenterology 2023;23(1) View
  88. Huang J, Yeung A, Armstrong D, Battarbee A, Cuadros J, Espinoza J, Kleinberg S, Mathioudakis N, Swerdlow M, Klonoff D. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. Journal of Diabetes Science and Technology 2023;17(1):224 View
  89. Yu Z, Luo W, Tse R, Pau G. DMNet: A Personalized Risk Assessment Framework for Elderly People With Type 2 Diabetes. IEEE Journal of Biomedical and Health Informatics 2023;27(3):1558 View
  90. Nemat H, Khadem H, Eissa M, Elliott J, Benaissa M. Blood Glucose Level Prediction: Advanced Deep-Ensemble Learning Approach. IEEE Journal of Biomedical and Health Informatics 2022;26(6):2758 View
  91. Khor S, Choi J, Won P, Ko S. Challenges and Strategies in Developing an Enzymatic Wearable Sweat Glucose Biosensor as a Practical Point-Of-Care Monitoring Tool for Type II Diabetes. Nanomaterials 2022;12(2):221 View
  92. Park G, Lee H, Lee M. Artificial Intelligence-based Healthcare Interventions: A Systematic Review. Korean Journal of Adult Nursing 2021;33(5):427 View
  93. Jia W, Fisher E. Application and prospect of artificial intellingence in diabetes care. Medical Review 2023;3(1):102 View
  94. Torrent-Sellens J, Jiménez-Zarco A, Saigí-Rubió F. Do People Trust in Robot-Assisted Surgery? Evidence from Europe. International Journal of Environmental Research and Public Health 2021;18(23):12519 View
  95. Ossai C, Wickramasinghe N. Sentiments prediction and thematic analysis for diabetes mobile apps using Embedded Deep Neural Networks and Latent Dirichlet Allocation. Artificial Intelligence in Medicine 2023;138:102509 View
  96. Fiedorova K, Augustynek M, Kubicek J, Kudrna P, Bibbo D. Review of present method of glucose from human blood and body fluids assessment. Biosensors and Bioelectronics 2022;211:114348 View
  97. Novitski P, Cohen C, Karasik A, Shalev V, Hodik G, Moskovitch R. All-cause mortality prediction in T2D patients with iTirps. Artificial Intelligence in Medicine 2022;130:102325 View
  98. Almásy M, Hörömpő A, Kiss D, Kertész G, Batyrshin I, Gomide F, Kreinovich V, Shahbazova S. A review on modeling tumor dynamics and agent reward functions in reinforcement learning based therapy optimization. Journal of Intelligent & Fuzzy Systems 2022;43(6):6939 View
  99. Novitski P, Cohen C, Karasik A, Hodik G, Moskovitch R. Temporal patterns selection for All-Cause Mortality prediction in T2D with ANNs. Journal of Biomedical Informatics 2022;134:104198 View
  100. Sikalidis A, Kristo A, Reaves S, Kurfess F, DeLay A, Vasilaky K, Donegan L. Capacity Strengthening Undertaking—Farm Organized Response of Workers against Risk for Diabetes: (C.S.U.—F.O.R.W.A.R.D. with Cal Poly)—A Concept Approach to Tackling Diabetes in Vulnerable and Underserved Farmworkers in California. Sensors 2022;22(21):8299 View
  101. Gudlavalleti R, Xi X, Legassey A, Chan P, Li J, Burgess D, Giardina C, Papadimitrakopoulos F, Jain F. Highly Miniaturized, Low-Power CMOS ASIC Chip for Long-Term Continuous Glucose Monitoring. Journal of Diabetes Science and Technology 2023:193229682311534 View
  102. Kulzer B. Wie profitieren Menschen mit Diabetes von Big Data und künstlicher Intelligenz?. Der Diabetologe 2021;17(8):799 View
  103. Xie X. WITHDRAWN: Real-Time Monitoring Of Big Data Sports Teaching Data Based On Complex Embedded System. Microprocessors and Microsystems 2022:104181 View
  104. Dénes-Fazakas L, Siket M, Szilágyi L, Kovács L, Eigner G. Detection of Physical Activity Using Machine Learning Methods Based on Continuous Blood Glucose Monitoring and Heart Rate Signals. Sensors 2022;22(21):8568 View
  105. Qiu F, Li J, Zhang R, Legerlotz K. Use of artificial neural networks in the prognosis of musculoskeletal diseases—a scoping review. BMC Musculoskeletal Disorders 2023;24(1) View
  106. Allam A, Feuerriegel S, Rebhan M, Krauthammer M. Analyzing Patient Trajectories With Artificial Intelligence. Journal of Medical Internet Research 2021;23(12):e29812 View
  107. Annuzzi G, Apicella A, Arpaia P, Bozzetto L, Criscuolo S, Benedetto E, Pesola M, Prevete R, Vallefuoco E. Impact of Nutritional Factors in Blood Glucose Prediction in Type 1 Diabetes Through Machine Learning. IEEE Access 2023;11:17104 View
  108. Makroum M, Adda M, Bouzouane A, Ibrahim H. Machine Learning and Smart Devices for Diabetes Management: Systematic Review. Sensors 2022;22(5):1843 View
  109. Zhang J, Zheng C, Yang J, Usama M. Research on an Interactive Question Answering System of Artificial Intelligence Customer Service Based on Word2Vec. International Journal of e-Collaboration 2022;18(2):1 View
  110. Zhu T, Li K, Herrero P, Georgiou P. Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation. IEEE Journal of Biomedical and Health Informatics 2021;25(4):1223 View
  111. Bisio A, Anderson S, Norlander L, O’Malley G, Robic J, Ogyaadu S, Hsu L, Levister C, Ekhlaspour L, Lam D, Levy C, Buckingham B, Breton M. Impact of a Novel Diabetes Support System on a Cohort of Individuals With Type 1 Diabetes Treated With Multiple Daily Injections: A Multicenter Randomized Study. Diabetes Care 2022;45(1):186 View
  112. Alqahtani A, Roy A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. Evidence-Based Complementary and Alternative Medicine 2022;2022:1 View
  113. Thomsen C, Hangaard S, Kronborg T, Vestergaard P, Hejlesen O, Jensen M. Time for Using Machine Learning for Dose Guidance in Titration of People With Type 2 Diabetes? A Systematic Review of Basal Insulin Dose Guidance. Journal of Diabetes Science and Technology 2022:193229682211459 View
  114. Della Cioppa A, De Falco I, Koutny T, Scafuri U, Ubl M, Tarantino E. Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes. Applied Soft Computing 2023;134:110012 View
  115. Zaidi S, Chandola V, Ibrahim M, Romanski B, Mastrandrea L, Singh T. Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients. Scientific Reports 2021;11(1) View
  116. Linan-Reyes M, Garrido-Zafra J, Gil-de-Castro A, Moreno-Munoz A. Energy Management Expert Assistant, a New Concept. Sensors 2021;21(17):5915 View
  117. Abiyev R, Altiparmak H, Abdullah L. Type-2 Fuzzy Neural System for Diagnosis of Diabetes. Mathematical Problems in Engineering 2021;2021:1 View
  118. Holzer R, Bloch W, Brinkmann C. Continuous Glucose Monitoring in Healthy Adults—Possible Applications in Health Care, Wellness, and Sports. Sensors 2022;22(5):2030 View
  119. Singla R, Aggarwal S, Bindra J, Garg A, Singla A. Developing clinical decision support system using machine learning methods for type 2 diabetes drug management. Indian Journal of Endocrinology and Metabolism 2022;26(1):44 View
  120. Balakrishnan P, Jacyshyn-Owen E, Eberl M, Friedrich B, Etter T. Real-world demographic patterns of users of a digital primary prevention service for diabetes. Cardiovascular Endocrinology & Metabolism 2022;12(1) View
  121. Parcerisas A, Contreras I, Delecourt A, Bertachi A, Beneyto A, Conget I, Viñals C, Giménez M, Vehi J. A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin. Sensors 2022;22(4):1665 View
  122. Noguer J, Contreras I, Mujahid O, Beneyto A, Vehi J. Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models. Sensors 2022;22(13):4944 View
  123. Mosa A, Thongmotai C, Islam H, Paul T, Hossain K, Mandhadi V. Evaluation of machine learning applications using real-world EHR data for predicting diabetes-related long-term complications. Journal of Business Analytics 2022;5(2):141 View
  124. Sepúlveda M, Arauna D, García F, Albala C, Palomo I, Fuentes E. Frailty in Aging and the Search for the Optimal Biomarker: A Review. Biomedicines 2022;10(6):1426 View
  125. Vargas E, Aiello E, Pinsker J, Teymourian H, Tehrani F, Church M, Laffel L, Doyle F, Patti M, Dassau E, Wang J. Development of a Novel Insulin Sensor for Clinical Decision-Making. Journal of Diabetes Science and Technology 2023;17(4):1029 View
  126. Vaz M, Lopes J, Peixoto H, Santos M. Predictive Analytics to support diabetic patient detection. Procedia Computer Science 2022;201:690 View
  127. César de Lima Araújo H, Silva Martins F, Tucunduva Philippi Cortese T, Locosselli G. Artificial intelligence in urban forestry—A systematic review. Urban Forestry & Urban Greening 2021;66:127410 View
  128. Lee A, Wong A, Hung T, Yan J, Yang S. Nurse-led Telehealth Intervention for Rehabilitation (Telerehabilitation) Among Community-Dwelling Patients With Chronic Diseases: Systematic Review and Meta-analysis. Journal of Medical Internet Research 2022;24(11):e40364 View
  129. Aktaş Yılmaz B, Yılmaz A. Artificial Intelligence Applications in Endocrinology. Journal of Ankara University Faculty of Medicine 2022;75(1):35 View
  130. Russo S, Bonassi S. Prospects and Pitfalls of Machine Learning in Nutritional Epidemiology. Nutrients 2022;14(9):1705 View
  131. Kushwaha S, Srivastava R, Jain R, Sagar V, Aggarwal A, Bhadada S, Khanna P. Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents. Computer Methods and Programs in Biomedicine 2022;226:107180 View
  132. Deniz-Garcia A, Fabelo H, Rodriguez-Almeida A, Zamora-Zamorano G, Castro-Fernandez M, Alberiche Ruano M, Solvoll T, Granja C, Schopf T, Callico G, Soguero-Ruiz C, Wägner A. Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges. Journal of Medical Internet Research 2023;25:e44030 View
  133. Hamasaki H. Efficacy of Wearable Devices to Measure and Promote Physical Activity in the Management of Diabetes. EMJ Diabetes 2018:62 View
  134. Gosak L, Martinović K, Lorber M, Stiglic G. Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature. Journal of Nursing Management 2022;30(8):3765 View
  135. Faccioli S, Prendin F, Facchinetti A, Sparacino G, Del Favero S. Combined Use of Glucose-Specific Model Identification and Alarm Strategy Based on Prediction-Funnel to Improve Online Forecasting of Hypoglycemic Events. Journal of Diabetes Science and Technology 2023;17(5):1295 View
  136. Cooke E, Smith N, Thomas S, Ruston C, Hothi S, Hughes D. An integrated discrete event simulation and particle swarm optimisation model for optimising efficiency of cancer diagnosis pathways. Healthcare Analytics 2022;2:100082 View
  137. Celik I, Dindar M, Muukkonen H, Järvelä S. The Promises and Challenges of Artificial Intelligence for Teachers: a Systematic Review of Research. TechTrends 2022;66(4):616 View
  138. Yilmaz E, Belue M, Turkbey B, Reinhold C, Choyke P. A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging. Canadian Association of Radiologists Journal 2023;74(3):534 View
  139. Cabrera A, Biagi L, Beneyto A, Estremera E, Contreras I, Giménez M, Conget I, Bondia J, Martín-Fernández J, Vehí J. Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis. Mathematics 2023;11(5):1241 View
  140. Shuvo M, Islam S. Deep Multitask Learning by Stacked Long Short-Term Memory for Predicting Personalized Blood Glucose Concentration. IEEE Journal of Biomedical and Health Informatics 2023;27(3):1612 View
  141. Chen L, Jiang M, Jia F, Liu G. Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework. Journal of Business & Industrial Marketing 2022;37(5):1025 View
  142. Tuppad A, Patil S. Machine learning for diabetes clinical decision support: a review. Advances in Computational Intelligence 2022;2(2) View
  143. Vervoort D, Tam D, Wijeysundera H. Health Technology Assessment for Cardiovascular Digital Health Technologies and Artificial Intelligence: Why Is It Different?. Canadian Journal of Cardiology 2022;38(2):259 View
  144. Pleus S, Freckmann G, Schauer S, Heinemann L, Ziegler R, Ji L, Mohan V, Calliari L, Hinzmann R. Self-Monitoring of Blood Glucose as an Integral Part in the Management of People with Type 2 Diabetes Mellitus. Diabetes Therapy 2022;13(5):829 View
  145. Camp E, Quon R, Sajatovic M, Briggs F, Brownrigg B, Janevic M, Meisenhelter S, Steimel S, Testorf M, Kiriakopoulos E, Mazanec M, Fraser R, Johnson E, Jobst B. Supervised machine learning to predict reduced depression severity in people with epilepsy through epilepsy self-management intervention. Epilepsy & Behavior 2022;127:108548 View
  146. Imrisek S, Lee M, Goldner D, Nagra H, Lavaysse L, Hoy-Rosas J, Dachis J, Sears L. Effects of a Novel Blood Glucose Forecasting Feature on Glycemic Management and Logging in Adults With Type 2 Diabetes Using One Drop: Retrospective Cohort Study. JMIR Diabetes 2022;7(2):e34624 View
  147. Oprescu A, Miró-Amarante G, García-Díaz L, Rey V, Chimenea-Toscano A, Martínez-Martínez R, Romero-Ternero M. Towards a data collection methodology for Responsible Artificial Intelligence in health: A prospective and qualitative study in pregnancy. Information Fusion 2022;83-84:53 View
  148. Mujahid O, Contreras I, Beneyto A, Conget I, Giménez M, Vehi J. Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models. Mathematics 2022;10(20):3741 View
  149. Xie Y, Lu L, Gao F, He S, Zhao H, Fang Y, Yang J, An Y, Ye Z, Dong Z. Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare. Current Medical Science 2021;41(6):1123 View
  150. Joshua S, Abbas W, Lee J. M-Healthcare Model: An Architecture for a Type 2 Diabetes Mellitus Mobile Application. Applied Sciences 2022;13(1):8 View
  151. Kalra S, Unnikrishnan A, Prasanna Kumar K, Sahay R, Chandalia H, Saboo B, Annamalai S, Kesavadev J, Shukla R, Wangnoo S, Baruah M, Jacob J, Arora S, Singla R, Sharma S, Damodaran S, Bantwal G. Addendum 1: Forum for Injection Technique and Therapy Expert Recommendations, India. Diabetes Therapy 2023;14(1):29 View
  152. Ahmed A, Aziz S, Abd-alrazaq A, Farooq F, Sheikh J. Overview of Artificial Intelligence–Driven Wearable Devices for Diabetes: Scoping Review. Journal of Medical Internet Research 2022;24(8):e36010 View
  153. Kumar Das S, Nayak K, Krishnaswamy P, Kumar V, Bhat N. Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices. ECS Sensors Plus 2022;1(3):031601 View
  154. Sharma V, Feldman M, Sharma R. Telehealth Technologies in Diabetes Self-management and Education. Journal of Diabetes Science and Technology 2024;18(1):148 View
  155. Juneja D, Gupta A, Singh O. Artificial intelligence in critically ill diabetic patients: current status and future prospects. Artificial Intelligence in Gastroenterology 2022;3(2):66 View
  156. Nabukenya J, Egwar A, Drumright L, Semwanga A, Kasasa S. Feasibility and utility of Point-of-Care electronic clinical data capture in Uganda’s healthcare system: a qualitative study. Journal of the American Medical Informatics Association 2023;30(5):932 View
  157. Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee H, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosensors and Bioelectronics 2023;229:115233 View
  158. Ansari R, Harris M, Hosseinzadeh H, Zwar N. Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes. Healthcare 2023;11(6):903 View
  159. Hu X, Li X, Wen S, Chen L. Predictive Modeling the Probability of Suffering from Metabolic Syndrome Using Machine Learning: A Population-Based Study. SSRN Electronic Journal 2022 View
  160. Hu X, Li X, Wen S, Chen L. Predictive Modeling the Probability of Suffering from Metabolic Syndrome Using Machine Learning: A Population-Based Study. SSRN Electronic Journal 2022 View
  161. Kushwaha S, Srivastava R, Jain R, Sagar V, Aggarwal A, Bhadada S, Khanna P. Harnessing Machine Learning Models for Non-Invasive Pre-Diabetes Screening in Children and Adolescents. SSRN Electronic Journal 2022 View
  162. Carpinteiro C, Lopes J, Abelha A, Santos M. A Comparative Study of Classification Algorithms for Early Detection of Diabetes. Procedia Computer Science 2023;220:868 View
  163. Della Cioppa A, De Falco I, Koutny T, Scafuri U, Ubl M, Tarantino E. Reducing High-Risk Glucose Forecasting Errors by Evolving Interpretable Models for Type 1 Diabetes. SSRN Electronic Journal 2022 View
  164. Mounadel A, Ech-Cheikh H, Lissane Elhaq S, Rachid A, Sadik M, Abdellaoui B. Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review. Environmental Technology Reviews 2023;12(1):316 View
  165. Robinson R, Liday C, Lee S, Williams I, Wright M, An S, Nguyen E. Artificial Intelligence in Health Care—Understanding Patient Information Needs and Designing Comprehensible Transparency: Qualitative Study. JMIR AI 2023;2:e46487 View
  166. Juneja D, Deepak D, Nasa P. What, why and how to monitor blood glucose in critically ill patients. World Journal of Diabetes 2023;14(5):528 View
  167. Rangel-Peña U, Zárate-Hernández L, Camacho-Mendoza R, Gómez-Castro C, González-Montiel S, Pescador-Rojas M, Meneses-Viveros A, Cruz-Borbolla J. Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 toxicity of organothiophosphates. Journal of Molecular Modeling 2023;29(7) View
  168. Cabrera A, Estremera E, Beneyto A, Biagi L, Contreras I, Martín-Fernández J, Vehí J. Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis. Mathematics 2023;11(21):4517 View
  169. Fujihara K, Sone H. Machine Learning Approach to Drug Treatment Strategy for Diabetes Care. Diabetes & Metabolism Journal 2023;47(3):325 View
  170. Ahmed A. Can artificial intelligence assist physicians in selecting the right medications for patients with diabetes mellitus, improve outcomes, and reduce financial burdens on health-care systems?. Advances in Biomedical and Health Sciences 2023;2(3):144 View
  171. Liu M, Liu C, Lin T, Ma Y. Implementing a Novel Machine Learning System for Nutrition Education in Diabetes Mellitus Nutritional Clinic: Predicting 1-Year Blood Glucose Control. Bioengineering 2023;10(10):1139 View
  172. Vargas E, Nandhakumar P, Ding S, Saha T, Wang J. Insulin detection in diabetes mellitus: challenges and new prospects. Nature Reviews Endocrinology 2023;19(8):487 View
  173. Nabukenya J, Drumright L, Alunyu A, Semwanga A. Critical risk and success factors for sustainability of an electronic health data capture, processing and dissemination platform for Uganda. Health Informatics Journal 2023;29(2):146045822311805 View
  174. Alanis A, Sanchez O, Vaca-González A, Rangel-Heras E. Intelligent Classification and Diagnosis of Diabetes and Impaired Glucose Tolerance Using Deep Neural Networks. Mathematics 2023;11(19):4065 View
  175. Khodve G, Banerjee S. Artificial Intelligence in Efficient Diabetes Care. Current Diabetes Reviews 2023;19(9) View
  176. Contreras I, Muñoz-Organero M, Beneyto A, Vehi J. Active Labeling Correction of Mealtimes and the Appearance of Types of Carbohydrates in Type 1 Diabetes Information Records. Mathematics 2023;11(19):4050 View
  177. Tanhapour M, Peimani M, Rostam Niakan Kalhori S, Nasli Esfahani E, Shakibian H, Mohammadzadeh N, Qorbani M. The effect of personalized intelligent digital systems for self-care training on type II diabetes: a systematic review and meta-analysis of clinical trials. Acta Diabetologica 2023;60(12):1599 View
  178. Zhu T, Li K, Georgiou P. Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes. IEEE Journal of Biomedical and Health Informatics 2023;27(10):5087 View
  179. Cai D, Wu W, Cescon M, Liu W, Ji L, Shi D. Data-enabled learning and control algorithms for intelligent glucose management: The state of the art. Annual Reviews in Control 2023;56:100897 View
  180. Huang S, Ke X, Huang Y, Wu Y, Yu X, Liu H, Liu D. A prediction model for moderate to severe cancer-related fatigue in colorectal cancer after chemotherapy: a prospective case‒control study. Supportive Care in Cancer 2023;31(7) View
  181. Salvioli S, Basile M, Bencivenga L, Carrino S, Conte M, Damanti S, De Lorenzo R, Fiorenzato E, Gialluisi A, Ingannato A, Antonini A, Baldini N, Capri M, Cenci S, Iacoviello L, Nacmias B, Olivieri F, Rengo G, Querini P, Lattanzio F. Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective. Ageing Research Reviews 2023;91:102044 View
  182. Ansari M, Chauhan W, Shoaib S, Alyahya S, Ali M, Ashraf H, Alomary M, Al-Suhaimi E. Emerging therapeutic options in the management of diabetes: recent trends, challenges and future directions. International Journal of Obesity 2023;47(12):1179 View
  183. Wang R, Xiong K, Wang Z, Wu D, Hu B, Ruan J, Sun C, Ma D, Li L, Liao S. Immunodiagnosis — the promise of personalized immunotherapy. Frontiers in Immunology 2023;14 View
  184. Li L, Cheng Y, Ji W, Liu M, Hu Z, Yang Y, Wang Y, Zhou Y. Machine learning for predicting diabetes risk in western China adults. Diabetology & Metabolic Syndrome 2023;15(1) View
  185. Prioleau T, Bartolome A, Comi R, Stanger C. DiaTrend: A dataset from advanced diabetes technology to enable development of novel analytic solutions. Scientific Data 2023;10(1) View
  186. Zou X, Liu Y, Ji L. Review: Machine learning in precision pharmacotherapy of type 2 diabetes—A promising future or a glimpse of hope?. DIGITAL HEALTH 2023;9 View
  187. AK S. USE OF ARTIFICIAL INTELLIGENCE IN HEALTH SERVICES MANAGEMENT IN TÜRKİYE. International Journal of Health Services Research and Policy 2023;8(2):139 View
  188. Abdulazeem H, Whitelaw S, Schauberger G, Klug S, Vathy-Fogarassy Á. A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data. PLOS ONE 2023;18(9):e0274276 View
  189. Niyitunga E. The 4IR-Health Service Delivery Nexus. International Journal of Public Administration in the Digital Age 2023;10(1):1 View
  190. Mora T, Roche D, Rodríguez-Sánchez B. Predicting the onset of diabetes-related complications after a diabetes diagnosis with machine learning algorithms. Diabetes Research and Clinical Practice 2023;204:110910 View
  191. Fujihara K, Yamada Harada M, Horikawa C, Iwanaga M, Tanaka H, Nomura H, Sui Y, Tanabe K, Yamada T, Kodama S, Kato K, Sone H. Machine learning approach to predict body weight in adults. Frontiers in Public Health 2023;11 View
  192. Rodríguez-Rodríguez I, Campo-Valera M, Rodríguez J, Lok Woo W. IoMT innovations in diabetes management: Predictive models using wearable data. Expert Systems with Applications 2024;238:121994 View
  193. Jaloli M, Cescon M. Reinforcement Learning for Multiple Daily Injection (MDI) Therapy in Type 1 Diabetes (T1D). BioMedInformatics 2023;3(2):422 View
  194. Liao X, Yao C, Zhang J, Liu L. Recent advancement in integrating artificial intelligence and information technology with real‐world data for clinical decision‐making in China: A scoping review. Journal of Evidence-Based Medicine 2023;16(4):534 View
  195. Zrubka Z, Kertész G, Gulácsi L, Czere J, Hölgyesi Á, Nezhad H, Mosavi A, Kovács L, Butte A, Péntek M. The Reporting Quality of Machine Learning Studies on Pediatric Diabetes Mellitus: Systematic Review. Journal of Medical Internet Research 2024;26:e47430 View
  196. Mackenzie S, Sainsbury C, Wake D. Diabetes and artificial intelligence beyond the closed loop: a review of the landscape, promise and challenges. Diabetologia 2024;67(2):223 View
  197. Wang B, Asan O, Zhang Y. Shaping the future of chronic disease management: Insights into patient needs for AI-based homecare systems. International Journal of Medical Informatics 2024;181:105301 View
  198. García-Jaramillo M, Luque C, León-Vargas F. Machine Learning and Deep Learning Techniques Applied to Diabetes Research: A Bibliometric Analysis. Journal of Diabetes Science and Technology 2024;18(2):287 View
  199. Dey A. ChatGPT in Diabetes Care: An Overview of the Evolution and Potential of Generative Artificial Intelligence Model Like ChatGPT in Augmenting Clinical and Patient Outcomes in the Management of Diabetes. International Journal of Diabetes and Technology 2023;2(2):66 View
  200. Murala D, Panda S, Dash S. MedMetaverse: Medical Care of Chronic Disease Patients and Managing Data Using Artificial Intelligence, Blockchain, and Wearable Devices State-of-the-Art Methodology. IEEE Access 2023;11:138954 View
  201. Dai D, Bo M, Ren X, Dai K. Application and exploration of artificial intelligence technology in urban ecosystem-based disaster risk reduction: A scoping review. Ecological Indicators 2024;158:111565 View
  202. Jacobs P, Herrero P, Facchinetti A, Vehi J, Kovatchev B, Breton M, Cinar A, Nikita K, Doyle F, Bondia J, Battelino T, Castle J, Zarkogianni K, Narayan R, Mosquera-Lopez C. Artificial Intelligence and Machine Learning for Improving Glycemic Control in Diabetes: Best Practices, Pitfalls, and Opportunities. IEEE Reviews in Biomedical Engineering 2024;17:19 View
  203. Ahmad M, Tan M, Bergman H, Shalhoub J, Davies A. The use of artificial intelligence in three-dimensional imaging modalities and diabetic foot disease: A systematic review. JVS-Vascular Insights 2024;2:100057 View
  204. Aldaghi T, Muzik J. Multicriteria Decision-Making in Diabetes Management and Decision Support: Systematic Review. JMIR Medical Informatics 2024;12:e47701 View
  205. Visan A, Negut I. Integrating Artificial Intelligence for Drug Discovery in the Context of Revolutionizing Drug Delivery. Life 2024;14(2):233 View
  206. Khalifa M, Albadawy M. Artificial intelligence for diabetes: Enhancing prevention, diagnosis, and effective management. Computer Methods and Programs in Biomedicine Update 2024;5:100141 View
  207. Ahmed B, Ali M, Masud M, Naznin M. Recent trends and techniques of blood glucose level prediction for diabetes control. Smart Health 2024;32:100457 View
  208. Han T, Wei W, Jiang W, Geng Y, Liu Z, Yang R, Jin C, Lei Y, Sun X, Xu J, Juan C, Sun C. The Future Landscape and Framework of Precision Nutrition. Engineering 2024 View
  209. Wu D, Mei Y, Sun Z, Duan H, Deng N. Multi-Feature Map Integrated Attention Model for Early Prediction of Type 2 Diabetes Using Irregular Health Examination Records. IEEE Journal of Biomedical and Health Informatics 2024;28(3):1656 View
  210. Khalifa M, Albadawy M. Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions. Computer Methods and Programs in Biomedicine Update 2024;5:100148 View
  211. Leal Filho W, Ribeiro P, Mazutti J, Lange Salvia A, Bonato Marcolin C, Lima Silva Borsatto J, Sharifi A, Sierra J, Luetz J, Pretorius R, Viera Trevisan L. Using artificial intelligence to implement the UN sustainable development goals at higher education institutions. International Journal of Sustainable Development & World Ecology 2024:1 View
  212. Spoladore D, Tosi M, Lorenzini E. Ontology-based decision support systems for diabetes nutrition therapy: A systematic literature review. Artificial Intelligence in Medicine 2024;151:102859 View
  213. Zhang D, Wu C, Yang Z, Yin H, Liu Y, Li W, Huang H, Jin Z. The application of artificial intelligence in EUS. Endoscopic Ultrasound 2024;13(2):65 View
  214. Dénes-Fazakas L, Simon B, Hartvég Á, Kovács L, Dulf É, Szilágyi L, Eigner G. Physical Activity Detection for Diabetes Mellitus Patients Using Recurrent Neural Networks. Sensors 2024;24(8):2412 View
  215. Yoon S, Goh H, Lee P, Tan H, Teh M, Lim D, Kwee A, Suresh C, Carmody D, Swee D, Tan S, Wong A, Choo C, Wee Z, Bee Y. Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence–Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study. JMIR Human Factors 2024;11:e50939 View
  216. Vyas P, Brandon K, Gephart S. A Scoping Review of Studies Using Artificial Intelligence Identifying Optimal Practice Patterns for Inpatients With Type 2 Diabetes That Lead to Positive Healthcare Outcomes. CIN: Computers, Informatics, Nursing 2024;42(5):396 View
  217. Annuzzi G, Apicella A, Arpaia P, Bozzetto L, Criscuolo S, De Benedetto E, Pesola M, Prevete R. Exploring Nutritional Influence on Blood Glucose Forecasting for Type 1 Diabetes Using Explainable AI. IEEE Journal of Biomedical and Health Informatics 2024;28(5):3123 View
  218. Dixon D, Sattar H, Moros N, Kesireddy S, Ahsan H, Lakkimsetti M, Fatima M, Doshi D, Sadhu K, Junaid Hassan M. Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review. Cureus 2024 View
  219. Soltanizadeh S, Naghibi S. Hybrid CNN-LSTM for Predicting Diabetes: A Review. Current Diabetes Reviews 2024;20(7) View
  220. Tabashum T, Snyder R, O'Brien M, Albert M. Machine Learning Models for Parkinson Disease: Systematic Review. JMIR Medical Informatics 2024;12:e50117 View
  221. Butunoi B, Stolojescu-Crisan C, Negru V. Short-term glucose prediction in Type 1 Diabetes. Procedia Computer Science 2024;238:41 View
  222. Boldina Y, Ivshin A. Machine learning opportunities to predict obstetric haemorrhages. Obstetrics, Gynecology and Reproduction 2024;18(3):365 View

Books/Policy Documents

  1. Shaban-Nejad A, Kamaleswaran R, Shin E, Akbilgic O. Biomedical Information Technology. View
  2. Ramyashree , Venugopala P, Barh D, Ashwini B. Advances in Artificial Intelligence and Data Engineering. View
  3. Kriještorac M, Halilović A, Kevric J. Advanced Technologies, Systems, and Applications IV -Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2019). View
  4. Li R. Advances in Artificial Intelligence, Software and Systems Engineering. View
  5. Singla S. Internet of Things Use Cases for the Healthcare Industry. View
  6. Contreras I, Bertachi A, Biagi L, Oviedo S, Ramkissoon C, Vehi J. Artificial Intelligence in Precision Health. View
  7. Wolkowicz K, Doyle III F, Dassau E. Encyclopedia of Systems and Control. View
  8. Agushaka J, Ezugwu A. Applied Informatics. View
  9. Jemima Jebaseeli T, Jasmine David D, Jegathesan V. Internet of Medical Things. View
  10. Vehi J, Mujahid O, Contreras I. Artificial Intelligence in Medicine. View
  11. Wolkowicz K, Doyle III F, Dassau E. Encyclopedia of Systems and Control. View
  12. Abd-Alrazaq A, Schneider J, Alhuwail D, Hamdi M, Al-Kuwari S, Al-Thani D, Househ M. Multiple Perspectives on Artificial Intelligence in Healthcare. View
  13. Kia N, Cavanagh J, Meacham H, Halvorsen B, Cabrera P, Bartram T. The Fourth Industrial Revolution. View
  14. Segato T, Serafim R, Fernandes S, Ralha C. Intelligent Systems. View
  15. Altıparmak H, Abiyev R, Tüzünkan M. Intelligent and Fuzzy Systems. View
  16. Geetanjali , Malviya R, Awasthi R, Sharma P, Kala N, Kumar V, Yadav S. Cognitive Intelligence and Big Data in Healthcare. View
  17. Yip M, Wang Z, Gutierrez L, Foo V, Lim J, Lim G, Gunasekaran D, Wong T, Ting D. Nanotechnology for Diabetes Management. View
  18. Kelly C, Brown A, Taylor J. Artificial Intelligence in Medicine. View
  19. Li S, Wang J. Diabetes Digital Health and Telehealth. View
  20. Yadav S, Kaushik A, Sharma S. IoT and Cloud Computing for Societal Good. View
  21. Vehi J, Mujahid O, Contreras I. Advanced Bioscience and Biosystems for Detection and Management of Diabetes. View
  22. Kinzel C, Pfannstiel M. Künstliche Intelligenz im Gesundheitswesen. View
  23. Reddy S, Sethi N, Rajender R, Vetukuri V. Third International Conference on Image Processing and Capsule Networks. View
  24. Kelly C, Brown A, Taylor J. Artificial Intelligence in Medicine. View
  25. Ming W, He Z. Advanced Bioscience and Biosystems for Detection and Management of Diabetes. View
  26. Vehi J, Mujahid O, Contreras I. Artificial Intelligence in Medicine. View
  27. Ghosh S, Dasgupta R. Machine Learning in Biological Sciences. View
  28. Xanthis C, Filos D, Chouvarda I. Comprehensive Clinical Approach to Diabetes During Pregnancy. View
  29. Belazoui A, Telli A, Arar C. International Conference on Managing Business Through Web Analytics. View
  30. Simon T, Zhang J, Wang S. Advanced Information Networking and Applications. View
  31. Muthusamy P, Boopathi Raja G, Sathya T, Nandhini P. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning. View
  32. Singh C, Thamizhamuthu R, Manjula S, Nidhya M. AI and IoT-Based Technologies for Precision Medicine. View
  33. El Sherbini A, Glicksberg B, Krittanawong C. Artificial Intelligence in Clinical Practice. View
  34. Christogianni A. Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications. View
  35. Xin Yi W, May Chong M, A/L Subarmaniyan S. Emerging Technologies for Digital Infrastructure Development. View
  36. Karalis V. From Current to Future Trends in Pharmaceutical Technology. View
  37. Singh K, Barak D. Driving Smart Medical Diagnosis Through AI-Powered Technologies and Applications. View
  38. Tornero-Costa R, Martinez-Millana A, Merino-Torres J. Explainable Artificial Intelligence and Process Mining Applications for Healthcare. View
  39. Sousa M, Sousa M, Secinaro S, Oppioli M. Proceedings of International Conference on Information Technology and Applications. View
  40. Zale A, Abusamaan M, Mathioudakis N. Diabetes Digital Health, Telehealth, and Artificial Intelligence. View