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

Public (e)Health, Digital Epidemiology and Public Health Informatics (1055) Infectious Diseases (non-STD/STI) (1738) Machine Learning (2173) Influenza and Influenza-Like-Illness (ILI) Surveillance (238) Wearable Devices and Sensors (1177) Surveillance Systems (519)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47879, first published .
Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study

Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study

Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study

Authors of this article:

Kamran Farooq1 Author Orcid Image ;   Melody Lim2 Author Orcid Image ;   Lawrence Dennison-Hall3 Author Orcid Image ;   Finn Janson3 Author Orcid Image ;   Aspen Hazel Olszewska4 Author Orcid Image ;   Muhammad Mamduh Ahmad Zabidi5 Author Orcid Image ;   Anna Haratym-Rojek4 Author Orcid Image ;   Karol Narowski6 Author Orcid Image ;   Barry Clinch3 Author Orcid Image ;   Marco Prunotto7, 8 Author Orcid Image ;   Devika Chawla2 Author Orcid Image ;   Victoria Hunter2 Author Orcid Image ;   Vincent Ukachukwu3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

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  1. Zhang X, Chen S, Li J, Liu D, Lai J, Song X, Hu R, Qiu Y, Chen K, Xu Y, Li X. One‐Step RAA and CRISPR‐Cas13a Method for Detecting Influenza B Virus. Microbial Biotechnology 2025;18(4) View
  2. Jlassi O, Hadid A, McDonald E, Ding Q, Phillipp C, Trottier A, Cheng M, Papenburg J, Libman M, Jensen D, Dixon P. Predicting viral respiratory tract infections using wearable biosensor monitoring during 3-minute constant rate stair stepping tests. Frontiers in Sensors 2026;7 View