Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review - Chunni Zhou; Liu Shuai; Hao Hu; Carolina Oi Lam Ung; Yunfeng Lai; Lijun Fan; Wei Du; Yan Wang; Meng Li (BMC Medical Informatics and Decision Making)
Abstract Background Pediatric asthma is a common chronic respiratory disease worldwide, and its acute exacerbation events significantly impact children’s health and quality of life. Machine learning, an advanced data analysis technique, has shown great potential in healthcare applications in recent years. This systematic review aims to assess the application of ML techniques in pediatric asthma exacerbation and explore their effectiveness and potential value. Methods Studies from four electronic databases, including PubMed, EBSCO, Elsevier, and Web of Science, from Jan 2000 to Jan 2025, were searched. Studies applying the ML methods for pediatric asthma exacerbation and published in English were eligible. The risk of bias and applicability of the included studies was assessed using the Effective Public Health Practice Project (EPHPP) quality assessment tool. Results A total of 23 studies were selected for inclusion in this review, covering different ML models such as decision trees, neural networks, and support vector machines. These studies focused on analyzing risk factors for asthma exacerbation, diagnosing and predicting, optimizing and allocating healthcare resources, and comprehensive asthma management. The results show that ML techniques have significant advantages in the application of pediatric asthma exacerbation and in the provision of personalized health care. Conclusions ML techniques show great promise for application in pediatric asthma exacerbations. With further research and clinical validation, these techniques are expected to provide strong support for diagnosis, personalized treatment, and long-term management of pediatric asthma exacerbation. Clinical trial number Not applicable, Prospero registration number CRD42024559232.