Published on in Vol 6, No 1 (2021): Jan-Mar

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22458, first published .
Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

Journals

  1. Duckworth C, Guy M, Kumaran A, O’Kane A, Ayobi A, Chapman A, Marshall P, Boniface M. Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations. Journal of Diabetes Science and Technology 2024;18(1):113 View
  2. 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
  3. Chiu I, Cheng C, Chang P, Li C, Cheng F, Lin C. Utilization of Personalized Machine-Learning to Screen for Dysglycemia from Ambulatory ECG, toward Noninvasive Blood Glucose Monitoring. Biosensors 2022;13(1):23 View
  4. Worth C, Nutter P, Dunne M, Salomon-Estebanez M, Banerjee I, Harper S. HYPO-CHEAT’s aggregated weekly visualisations of risk reduce real world hypoglycaemia. DIGITAL HEALTH 2022;8:205520762211297 View
  5. 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
  6. Worth C, Hoskyns L, Salomon-Estebanez M, Nutter P, Harper S, Derks T, Beardsall K, Banerjee I. Continuous glucose monitoring for children with hypoglycaemia: Evidence in 2023. Frontiers in Endocrinology 2023;14 View
  7. Gautier T, Ziegler L, Gerber M, Campos-Náñez E, Patek S. Artificial intelligence and diabetes technology: A review. Metabolism 2021;124:154872 View
  8. Zale A, Abusamaan M, McGready J, Mathioudakis N. Development and validation of a machine learning model for classification of next glucose measurement in hospitalized patients. eClinicalMedicine 2022;44:101290 View
  9. Xu N, Nguyen K, DuBord A, Pickup J, Sherr J, Teymourian H, Cengiz E, Ginsberg B, Cobelli C, Ahn D, Bellazzi R, Bequette B, Gandrud Pickett L, Parks L, Spanakis E, Masharani U, Akturk H, Melish J, Kim S, Kang G, Klonoff D. Diabetes Technology Meeting 2021. Journal of Diabetes Science and Technology 2022;16(4):1016 View
  10. Bartolome A, Prioleau T. A computational framework for discovering digital biomarkers of glycemic control. npj Digital Medicine 2022;5(1) View
  11. Berikov V, Kutnenko O, Semenova J, Klimontov V. Machine Learning Models for Nocturnal Hypoglycemia Prediction in Hospitalized Patients with Type 1 Diabetes. Journal of Personalized Medicine 2022;12(8):1262 View
  12. Chang V, Kandadai K, Xu Q, Guan S. Development of a Diabetes Diagnosis System Using Machine Learning Algorithms. International Journal of Distributed Systems and Technologies 2022;13(1):1 View
  13. Stredny C, Rotenberg A, Leviton A, Loddenkemper T. Systemic inflammation as a biomarker of seizure propensity and a target for treatment to reduce seizure propensity. Epilepsia Open 2023;8(1):221 View
  14. Zaitcev A, Eissa M, Hui Z, Good T, Elliott J, Benaissa M. Automatic inference of hypoglycemia causes in type 1 diabetes: a feasibility study. Frontiers in Clinical Diabetes and Healthcare 2023;4 View
  15. 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
  16. Fujihara K, Sone H. Machine Learning Approach to Drug Treatment Strategy for Diabetes Care. Diabetes & Metabolism Journal 2023;47(3):325 View
  17. Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior K, Poirrier J, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(1):63 View
  18. Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Reports Medicine 2023;4(10):101213 View
  19. Liu K, Li L, Ma Y, Jiang J, Liu Z, Ye Z, Liu S, Pu C, Chen C, Wan Y. Machine Learning Models for Blood Glucose Level Prediction in Patients With Diabetes Mellitus: Systematic Review and Network Meta-Analysis. JMIR Medical Informatics 2023;11:e47833 View
  20. Alexiadis A, Tsanas A, Shtika L, Efopoulos V, Votis K, Tzovaras D, Triantafyllidis A. Next-Day Prediction of Hypoglycaemic Episodes Based on the Use of a Mobile App for Diabetes Self-Management. IEEE Access 2024;12:7469 View
  21. Kozinetz R, Berikov V, Semenova J, Klimontov V. Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes. Diagnostics 2024;14(7):740 View
  22. Shi M, Yang A, Lau E, Luk A, Ma R, Kong A, Wong R, Chan J, Chan J, Chow E. A novel electronic health record-based, machine-learning model to predict severe hypoglycemia leading to hospitalizations in older adults with diabetes: A territory-wide cohort and modeling study. PLOS Medicine 2024;21(4):e1004369 View
  23. Sheng B, Pushpanathan K, Guan Z, Lim Q, Lim Z, Yew S, Goh J, Bee Y, Sabanayagam C, Sevdalis N, Lim C, Lim C, Shaw J, Jia W, Ekinci E, Simó R, Lim L, Li H, Tham Y. Artificial intelligence for diabetes care: current and future prospects. The Lancet Diabetes & Endocrinology 2024;12(8):569 View
  24. Subramanian D, Sonabend R, Singh I. A Machine Learning Model for Risk Stratification of Postdiagnosis Diabetic Ketoacidosis Hospitalization in Pediatric Type 1 Diabetes: Retrospective Study. JMIR Diabetes 2024;9:e53338 View
  25. Karasneh R, Al-Azzam S, Alzoubi K, Ebbini M, Alselwi A, Rahhal D, Kabbaha S, Aldeyab M, Badr A. Predicting hypoglycemia in ICU patients: a machine learning approach. Expert Review of Endocrinology & Metabolism 2024;19(5):459 View
  26. Dhankhar S, Garg N, Chauhan S, Saini M. Role of Artificial Intelligence in Diabetic Wound Screening and Early Detection. Current Biotechnology 2024;13(2):93 View
  27. Gaikwad S, Bontha M, Devi S, Dumbre D. Improving Clinical Preparedness: Community Health Nurses and Early Hypoglycemia Prediction in Type 2 Diabetes Using Hybrid Machine Learning Techniques. Public Health Nursing 2024 View
  28. Chushig-Muzo D, Calero-Díaz H, Fabelo H, Årsand E, van Dijk P, Soguero-Ruiz C. Characterizing the Impact of Physical Activity on Patients with Type 1 Diabetes Using Statistical and Machine Learning Models. Applied Sciences 2024;14(21):9870 View
  29. Song H, Han J, Cho S, Im S, Kim Y, Park J. Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram. Diagnostics 2024;14(22):2489 View

Books/Policy Documents

  1. Hong N, Park Y, You S, Rhee Y. Artificial Intelligence in Medicine. View
  2. Hong N, Park Y, You S, Rhee Y. Artificial Intelligence in Medicine. View