ArticleAnnals of translational medicine2020
The role of artificial intelligence in identifying asthma in pediatric inpatient setting.
Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- Artificial intelligence-based models in predicting acute exacerbations and diagnosing pediatric asthma: a systematic review and meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence.BMC medical research methodology · 2025Pooled it
- The evolution of scientific knowledge in childhood asthma over time.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- AI-powered surveillance of bronchiolitis in the Nirsevimab era: comparative performance of machine learning, deep learning, and large language models on free-text ED records.BMC emergency medicine · 2026Article
- Impact of varying childhood asthma definitions on incidence and clinical outcomes.Annals of allergy, asthma & immunology : official publication of the American College of Allergy, Asthma, & Immunology · 2026Article
- Humanizing the pediatric intensive care unit through new technologies and artificial intelligence.Frontiers in health services · 2026Article
- The pediatric AI readiness framework: bridging evidence to practice in pediatric artificial intelligence.Frontiers in artificial intelligence · 2026Article
- Point of Care Testing, Rapid Next Generation Sequencing and Artificial Intelligence in Pediatric and Neonatal Healthcare: A Narrative Review.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Review
- Assessing the Role of Large Language Models Between ChatGPT and DeepSeek in Asthma Education for Bilingual Individuals: Comparative Study.JMIR medical informatics · 2025Article
- AI model for predicting asthma prognosis in children.The journal of allergy and clinical immunology. Global · 2025Article
- Ethical and Practical Considerations of Artificial Intelligence in Pediatric Medicine: A Systematic Review.Cureus · 2025Review
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis.Journal of medical Internet research · 2024Article
- Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.JMIR research protocols · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Article
- Machine learning is an effective method to predict the 90-day prognosis of patients with transient ischemic attack and minor stroke.BMC medical research methodology · 2022Article
- Integration of Artificial Intelligence, Blockchain, and Wearable Technology for Chronic Disease Management: A New Paradigm in Smart Healthcare.Current medical science · 2021Article
- Article
Corrections and comments
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe incidence of asthma in Chinese children has rapidly increased as a result of inadequate management. This is mainly due to the failure of many primary-level pediatricians to distinguish asthma from common respiratory diseases, such as bronchitis and pneumonia. Such misdiagnoses often lead to the abuse of antibiotics and systemic glucocorticoids. Additionally, if asthma is not diagnosed early, chronic airway inflammation results in lesions that not only hamper children's athletic abilities, but serve as the primary cause for adult chronic airway diseases, such as chronic obstructive pulmonary disease (COPD).
methodsA number of machine learning-based models including CatBoost, Logistic Regression, Naïve Bayes, and Support Vector Machines (SVM) have been developed to identify asthma via utilizing retrospective electronic medical records (EMRs) of patients. These models were evaluated independently using EMRs from both the Pulmonology Department and other departments of the Children's Hospital, Zhejiang University School of Medicine, China.
resultsTwo independent test sets were applied for performance evaluation. TestSet-1 consisted of 325 positive asthma cases and 428 negative cases from the Pulmonology Department. TestSet-2 was composed of 2,123 cases from non-pulmonology departments, and included 337 positive and 1,786 negative cases. Experimental results showed that the CatBoost model outperformed other models on both test sets with an accuracy of 84.7% and an area under the curve (AUC) of 90.9% on TestSet-1, and an accuracy of 96.7% and an AUC of 98.1% on TestSet-2.
conclusionsThe artificial intelligence (AI) model could rapidly and accurately identify asthma in general medical wards of children, and may aid primary pediatricians in the correct diagnoses of asthma. It possesses great clinical value and practical significance in improving the control rate of asthma in children, optimizing medical resources, and limiting the abuse of antibiotics and systemic glucocorticoids.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.