A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism

Semisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient...

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Vydané v:	Journal of medical Internet research Ročník 22; číslo 8; s. e18912
Hlavní autori:	Woldaregay, Ashenafi Zebene, Launonen, Ilkka Kalervo, Albers, David, Igual, Jorge, Årsand, Eirik, Hartvigsen, Gunnar
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Canada Gunther Eysenbach MD MPH, Associate Professor 12.08.2020 JMIR Publications
Predmet:	Algorithms Blood Carbohydrates Data Datasets Degradation Description Diabetes Diabetes Complications - complications Diabetes Mellitus, Type 1 - complications Diabetics Diet Glucose Groups Health problems Health services Health status Humans Hyperglycemia Incidence Infections Infectious diseases Influenza Insulin Machine learning Machine Learning - standards Methods Multimedia Original Paper Physical activity Physical fitness Precision Medicine - methods Public health Suitability Support vector machines Tests Time Type 1 diabetes mellitus self-recorded health data type 1 diabetes decision support techniques infection detection syndromic surveillance outbreak detection system
ISSN:	1438-8871, 1439-4456, 1438-8871
On-line prístup:	Získať plný text
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