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

Reviews on Diabetes Technologies and Innovations (20) Artificial Intelligence (1194) Diabetes Prevention (77)

Published on in Vol 7, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34699, first published .
Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review

Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review

Type 1 Diabetes Hypoglycemia Prediction Algorithms: Systematic Review

Authors of this article:

Stella Tsichlaki1 Author Orcid Image ;   Lefteris Koumakis2 Author Orcid Image ;   Manolis Tsiknakis1, 2 Author Orcid Image
Article Authors Cited by (14) Tweetations (2) Metrics

Journals

  1. D'Antoni F, Petrosino L, Marchetti A, Bacco L, Pieralice S, Vollero L, Pozzilli P, Piemonte V, Merone M. Layered Meta-Learning Algorithm for Predicting Adverse Events in Type 1 Diabetes. IEEE Access 2023;11:9074 View
  2. Lehmann V, Zueger T, Maritsch M, Kraus M, Albrecht C, Bérubé C, Feuerriegel S, Wortmann F, Kowatsch T, Styger N, Lagger S, Laimer M, Fleisch E, Stettler C. Machine learning for non‐invasive sensing of hypoglycaemia while driving in people with diabetes. Diabetes, Obesity and Metabolism 2023;25(6):1668 View
  3. Detopoulou P, Voulgaridou G, Moschos P, Levidi D, Anastasiou T, Dedes V, Diplari E, Fourfouri N, Giaginis C, Panoutsopoulos G, Papadopoulou S. Artificial intelligence, nutrition, and ethical issues: A mini-review. Clinical Nutrition Open Science 2023;50:46 View
  4. Chen E, Prakash S, Janapa Reddi V, Kim D, Rajpurkar P. A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring. Nature Biomedical Engineering 2023 View
  5. Litvinova O, Eitenberger M, Bilir A, Yeung A, Parvanov E, MohanaSundaram A, Horbańczuk J, Atanasov A, Willschke H. Patent analysis of digital sensors for continuous glucose monitoring. Frontiers in Public Health 2023;11 View
  6. 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
  7. Marx A, Di Stefano F, Leutheuser H, Chin-Cheong K, Pfister M, Burckhardt M, Bachmann S, Vogt J. Blood glucose forecasting from temporal and static information in children with T1D. Frontiers in Pediatrics 2023;11 View
  8. Freeman N, Muthukkumar R, Weinstock R, Wickerhauser M, Kahkoska A. Use of machine learning to identify characteristics associated with severe hypoglycemia in older adults with type 1 diabetes: a post-hoc analysis of a case–control study. BMJ Open Diabetes Research & Care 2024;12(1):e003748 View
  9. Butunoi B, Stolojescu-Crisan C, Negru V. Short-term glucose prediction in Type 1 Diabetes. Procedia Computer Science 2024;238:41 View

Books/Policy Documents

  1. Katsarou D, Georga E, Christou M, Tigas S, Papaloukas C, Fotiadis D. Pervasive Computing Technologies for Healthcare. View
  2. Choudhury A, Sarma K. Investigations in Pattern Recognition and Computer Vision for Industry 4.0. View