Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ryan G. Paul"'
Autor:
Mercedes J. Burnside, Dana M. Lewis, Hamish R. Crocket, Renee A. Meier, Jonathan A. Williman, Olivia J. Sanders, Craig A. Jefferies, Ann M. Faherty, Ryan G. Paul, Claire S. Lever, Sarah K.J. Price, Carla M. Frewen, Shirley D. Jones, Tim C. Gunn, Christina Lampey, Benjamin J. Wheeler, Martin I. de Bock
Publikováno v:
New England Journal of Medicine. 387:869-881
Open-source automated insulin delivery (AID) systems are used by many patients with type 1 diabetes. Data are needed on the efficacy and safety of an open-source AID system.In this multicenter, open-label, randomized, controlled trial, we assigned pa
Autor:
Hamish Crocket, Ann Faherty, Renée Wilson, Mercedes Burnside, Jonathan Williman, Martin de Bock, Dana Lewis, Craig Jefferies, Benjamin J Wheeler, Ryan G. Paul, Claire Lever, Shirley Jones, Olivia Sanders, Sarah Price, Carla M. Frewen
Publikováno v:
Diabetic Medicine. 39
Background Open-source automated insulin delivery (AID) is a user-driven treatment modality used by thousands globally. Health care professionals' (HCPs) ability to support users of this technology is limited by a lack of knowledge of these systems.
Autor:
Craig Jefferies, Martin de Bock, Jonathan Williman, Renée Wilson, Hamish Crocket, Benjamin J Wheeler, Ryan G. Paul, Mercedes Burnside, Dana Lewis
Publikováno v:
Journal of Diabetes and Metabolic Disorders
Background Commercialised automated insulin delivery (AID) systems have demonstrated improved outcomes in type 1 diabetes (T1D), however, they have limited capacity for algorithm individualisation, and can be prohibitively expensive if an individual
Publikováno v:
Sports Medicine-Open
Background In a randomised, counterbalanced, crossover design, eight men with type 1 diabetes (T1D; mean ± SD age, 27.6 ± 11.4 years) reduced insulin (INS) by 50% of their normal dose or consumed carbohydrates equivalent to 1 g of carbohydrate per
Publikováno v:
Internal medicine journalReferences. 51(5)
BACKGROUND It is well known that tight glycaemic control reduces all-cause mortality and the development of microvascular complications in patients with type 1 diabetes mellitus (T1D), but that effective glycaemic control is difficult to achieve in d
Publikováno v:
PLoS ONE, Vol 14, Iss 12, p e0225613 (2019)
PLoS ONE
PLoS ONE
Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hy