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

Formative Evaluation of Digital Health Interventions (2690) Clinical Information and Decision Making (1548) Machine Learning (1627) Artificial Intelligence (1603) Surveillance Systems (483) Clinical Informatics (1107) Digital Health, Telehealth and e-Innovation in Clinical Settings (457) Infectious Diseases (non-STD/STI) (1633)

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
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