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...

Full description

Saved in:

Bibliographic Details
Published in:	Journal of medical Internet research Vol. 22; no. 8; p. e18912
Main Authors:	Woldaregay, Ashenafi Zebene, Launonen, Ilkka Kalervo, Albers, David, Igual, Jorge, Årsand, Eirik, Hartvigsen, Gunnar
Format:	Journal Article
Language:	English
Published:	Canada Gunther Eysenbach MD MPH, Associate Professor 12.08.2020 JMIR Publications
Subjects:	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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!