Applications of machine learning approaches for pediatric asthma exacerbation management: a systematic review
Chunni Zhou1, Liu Shuai1, Hao Hu2
1School of Public Health, Southeast University, 87, Dingjiaqiao Road, Gulou District, Nanjing, 210009, China.
Insights
Machine learning (ML) techniques show significant promise for managing pediatric asthma exacerbations. These advanced data analysis methods offer advantages in diagnosis, personalized treatment, and long-term care for children with asthma.
Area of Science:
- Medical Informatics
- Computational Biology
- Pediatric Pulmonology
Background:
- Pediatric asthma exacerbations pose a significant global health challenge, impacting children's well-being and quality of life.
- Machine learning (ML), a sophisticated data analysis approach, is increasingly recognized for its potential in healthcare.
- This review systematically evaluates ML applications in pediatric asthma exacerbation management.
Purpose of the Study:
- To assess the application of ML techniques in pediatric asthma exacerbation.
- To explore the effectiveness and potential value of ML in this clinical area.
- To provide insights into advanced data analysis for pediatric respiratory health.
Main Methods:
- A systematic literature search was conducted across PubMed, EBSCO, Elsevier, and Web of Science databases (Jan 2000 - Jan 2025).
- Eligible studies involved ML methods applied to pediatric asthma exacerbation and were published in English.
- Study quality was assessed using the Effective Public Health Practice Project (EPHPP) tool.
Main Results:
- Twenty-three studies were included, utilizing various ML models like decision trees, neural networks, and support vector machines.
- ML applications focused on risk factor analysis, diagnosis, prediction, healthcare resource optimization, and comprehensive management.
- ML techniques demonstrated significant advantages in pediatric asthma exacerbation management and personalized healthcare delivery.
Conclusions:
- Machine learning techniques hold substantial promise for pediatric asthma exacerbations.
- Further research and clinical validation are crucial for robust implementation.
- ML is expected to significantly support diagnosis, personalized treatment, and long-term management strategies.
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.
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