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

Machine Learning (1662) Decision Support for Health Professionals (1310) Electronic Health Records (1139) Secondary Use of Clinical Data for Research and Surveillance (403) Clinical Information and Decision Making (1584) Tools, Programs and Algorithms (312)

Published on in Vol 10, No 1 (2022): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28934, first published .
Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach

Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach

Evaluation of the Need for Intensive Care in Children With Pneumonia: Machine Learning Approach

Authors of this article:

Yun-Chung Liu1, 2 Author Orcid Image ;   Hao-Yuan Cheng1, 3 Author Orcid Image ;   Tu-Hsuan Chang4 Author Orcid Image ;   Te-Wei Ho5 Author Orcid Image ;   Ting-Chi Liu6, 7 Author Orcid Image ;   Ting-Yu Yen1 Author Orcid Image ;   Chia-Ching Chou6 Author Orcid Image ;   Luan-Yin Chang1 Author Orcid Image ;   Feipei Lai2, 8, 9 Author Orcid Image
Article Authors Cited by (10) Tweetations (4) Metrics

Journals

  1. Gottlieb E, Samuel M, Bonventre J, Celi L, Mattie H. Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit. Advances in Chronic Kidney Disease 2022;29(5):431 View
  2. Gawronski O, Latour J, Cecchetti C, Iula A, Ravà L, Ciofi degli Atti M, Dall’Oglio I, Tiozzo E, Raponi M, Parshuram C. Escalation of care in children at high risk of clinical deterioration in a tertiary care children’s hospital using the Bedside Pediatric Early Warning System. BMC Pediatrics 2022;22(1) View
  3. Chang T, Liu Y, Lin S, Chiu P, Chou C, Chang L, Lai F. Clinical characteristics of hospitalized children with community-acquired pneumonia and respiratory infections: Using machine learning approaches to support pathogen prediction at admission. Journal of Microbiology, Immunology and Infection 2023;56(4):772 View
  4. Lin S, Wu J, Liu Y, Chiu P, Chang T, Wu E, Chou C, Chang L, Lai F. Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit. Pediatric Pulmonology 2024;59(5):1256 View
  5. Dube S, Seboka B, Demeke A, Feleke M, Jarso A, Bati A, Udo E, Markos S, Kassaw C, Yeheyis T, Debebe A, Gechere E, Dessie Y. Admission outcomes and their associated factors among children admitted to the paediatric emergency unit within 24 hours of Dilla University Referral Hospital, Ethiopia, 2023: a cross-sectional study. BMJ Open 2025;15(1):e091359 View
  6. Ying Z, Song L, Jin Z. Application of artificial intelligence in pediatric wheezing illnesses. Chinese Journal of Academic Radiology 2025 View
  7. Shen L, Wu J, Lu M, Jiang Y, Zhang X, Xu Q, Ran S. Advancing risk factor identification for pediatric lobar pneumonia: the promise of machine learning technologies. Frontiers in Pediatrics 2025;13 View
  8. Serin O, Akbasli I, Cetin S, Koseoglu B, Deveci A, Ugur M, Ozsurekci Y. Predicting Escalation of Care for Childhood Pneumonia Using Machine Learning: Retrospective Analysis and Model Development. JMIRx Med 2025;6:e57719 View
  9. Huang G, Zhu W, Wang Y, Wan Y, Chen K, Su Y, Su W, Li L, Liu P, dong Guo X. Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?. BMC Medical Informatics and Decision Making 2025;25(1) View
  10. 熊 锦. Advances in Laboratory Markers for Predicting Severity of Pediatric <i>Mycoplasma </i><i>p</i><i>neumoniae</i> Pneumonia. Advances in Clinical Medicine 2025;15(04):442 View