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

Formative Evaluation of Digital Health Interventions (2496) Clinical Information and Decision Making (1438) Machine Learning (1505) Artificial Intelligence (1430) Surveillance Systems (467) Clinical Informatics (1050) Digital Health, Telehealth and e-Innovation in Clinical Settings (421) Infectious Diseases (non-STD/STI) (1594)

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 Tweetations (4) Metrics
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.