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

Formative Evaluation of Digital Health Interventions (2973) Clinical Information and Decision Making (1754) Machine Learning (1872) Artificial Intelligence (1897) Surveillance Systems (502) Clinical Informatics (1214) Digital Health, Telehealth and e-Innovation in Clinical Settings (513) Infectious Diseases (non-STD/STI) (1680)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/52412, first published .
Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study

Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study

Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study

Authors of this article:

Shota Kawamoto1 Author Orcid Image ;   Yoshihiko Morikawa1 Author Orcid Image ;   Naohisa Yahagi1 Author Orcid Image
Article Authors Cited by (2) Tweetations (4) Metrics

Journals

  1. Kawamoto S, Morikawa Y, Yahagi N. Development and Validation of a Temporal Progression-based Risk Assessment Tool for Respiratory Syncytial Virus Infection in Infants. Pediatric Infectious Disease Journal 2025 View
  2. Livieratos A, Kagadis G, Gogos C, Akinosoglou K. AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review. Pathogens 2025;14(8):748 View