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

Clinical Information and Decision Making (1407) Reviews on Diabetes Technologies and Innovations (20) Diabetes Health Services and System Innovations (117)

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 (304) Tweetations (62) Metrics

Journals

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  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
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  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
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  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
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  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
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  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
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  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) 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
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  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, Chen J, Sun C. The Future Landscape and Framework of Precision Nutrition. Engineering 2024 View
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  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;31(6):726 View
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  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
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  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
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  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
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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
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