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

Formative Evaluation of Digital Health Interventions (2758) Clinical Information and Decision Making (1601) Machine Learning (1680) Artificial Intelligence (1660) Surveillance Systems (489) Clinical Informatics (1129) Digital Health, Telehealth and e-Innovation in Clinical Settings (467) Infectious Diseases (non-STD/STI) (1646)

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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JMIR Formative Res: Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study https://t.co/zCBnYAlotr https://t.co/1kxYokWmlx

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Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study https://t.co/AtF08Hiylj

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