Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes

Despite the increasing adoption of insulin pumps and continuous glucose monitoring devices, most people with type 1 diabetes do not achieve their glycemic goals 1 . This could be related to a lack of expertise or inadequate time for clinicians to analyze complex sensor-augmented pump data. We tested...

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Veröffentlicht in:	Nature medicine Jg. 26; H. 9; S. 1380 - 1384
Hauptverfasser:	Nimri, Revital, Battelino, Tadej, Laffel, Lori M., Slover, Robert H., Schatz, Desmond, Weinzimer, Stuart A., Dovc, Klemen, Danne, Thomas, Phillip, Moshe
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Nature Publishing Group US 01.09.2020 Nature Publishing Group
Schlagworte:	692/699/2743/137/1418 692/700/565 Adolescent Artificial Intelligence Automation Biomedical and Life Sciences Biomedicine Blood Glucose - analysis Blood Glucose Self-Monitoring - instrumentation Blood Glucose Self-Monitoring - methods Cancer Research Care and treatment Child Decision support systems Decision Support Systems, Clinical - instrumentation Diabetes Diabetes mellitus (insulin dependent) Diabetes Mellitus, Type 1 - drug therapy Diabetes therapy Diabetic ketoacidosis Dosage Glucose Glucose monitoring Humans Hypoglycemia Infectious Diseases Insulin Insulin - administration & dosage Insulin - therapeutic use Insulin Infusion Systems Insulin pumps Ketoacidosis Letter Metabolic Diseases Molecular Medicine Neurosciences Optimization Physicians Randomization Technology application Teenagers Titration Treatment Outcome Type 1 diabetes Young Adult Youth United States Europe Israel
ISSN:	1078-8956, 1546-170X, 1546-170X
Online-Zugang:	Volltext
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