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

New Methods (946) Decision Support for Health Professionals (1191) Machine Learning (1471) Hygiene and Infection Prevention (318) Infectious Diseases (non-STD/STI) (1576) Surveillance Systems (462)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18912, first published .
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

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

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

Authors of this article:

Ashenafi Zebene Woldaregay1 Author Orcid Image ;   Ilkka Kalervo Launonen2 Author Orcid Image ;   David Albers3, 4 Author Orcid Image ;   Jorge Igual5 Author Orcid Image ;   Eirik Årsand1 Author Orcid Image ;   Gunnar Hartvigsen1 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Gupta D, Choudhury A, Gupta U, Singh P, Prasad M. Computational approach to clinical diagnosis of diabetes disease: a comparative study. Multimedia Tools and Applications 2021;80(20):30091 View
  2. Afsaneh E, Sharifdini A, Ghazzaghi H, Ghobadi M. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review. Diabetology & Metabolic Syndrome 2022;14(1) View