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

Diabetes Reviews and Scoping Studies (29) Machine Learning (1401) Glucose Tracking and Self-Monitoring of Blood Glucose (152)

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

Authors of this article:

Satoru Kodama1 Author Orcid Image ;   Kazuya Fujihara2 Author Orcid Image ;   Haruka Shiozaki2 Author Orcid Image ;   Chika Horikawa3 Author Orcid Image ;   Mayuko Harada Yamada2 Author Orcid Image ;   Takaaki Sato2 Author Orcid Image ;   Yuta Yaguchi2 Author Orcid Image ;   Masahiko Yamamoto2 Author Orcid Image ;   Masaru Kitazawa2 Author Orcid Image ;   Midori Iwanaga2 Author Orcid Image ;   Yasuhiro Matsubayashi2 Author Orcid Image ;   Hirohito Sone2 Author Orcid Image
Article Authors Cited by (25) Tweetations (4) Metrics

Journals

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  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
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  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

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