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

Formative Evaluation of Digital Health Interventions (3384) Clinical Information and Decision Making (2029) Machine Learning (2194) Artificial Intelligence (2282) Surveillance Systems (519) Clinical Informatics (1399) Digital Health, Telehealth and e-Innovation in Clinical Settings (576) Infectious Diseases (non-STD/STI) (1742)

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 (6) 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;44(12):1145 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
  3. Kawamoto S, Morikawa Y, Yahagi N. Reducing invasive RSV diagnostic testing with machine learning: A retrospective validation study. Journal of Infection and Public Health 2025;18(12):102967 View
  4. Iannone S, Kaur A, Johnson K. Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions. Journal of General Internal Medicine 2026;41(2):364 View
  5. 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
  6. Miyagi Y, Morimoto Y, Satake E, Iwashima S, Yano Y, Urabe R, Kitagawa A, Kato H, Kin K. Identification of key clinical features for pediatric respiratory syncytial virus infection using machine learning. BMC Pediatrics 2026 View