Application of Machine Learning Algorithms in the Interpretation of Pediatric Asthma Pulmonary Function Data: Current Status and Future Prospects

This review examines the application of machine learning algorithms in the interpretation of pulmonary function data in children with asthma, highlighting the significance of this research area in improving diagnostic and treatment outcomes. Pediatric asthma presents unique challenges in pulmonary function assessment due to the variability in disease presentation and the need for age-appropriate evaluation techniques. Current research has demonstrated the potential of various machine learning algorithms to enhance the classification, prediction, and personalized treatment of asthma-related pulmonary function data. However, despite promising advancements, several issues remain, including algorithm interpretability, data quality, and integration into clinical practice. This review aims to provide a comprehensive overview of the strengths and limitations of different machine learning approaches in this context, while also discussing the future directions for research and application in pediatric asthma management. By addressing these aspects, we hope to contribute to the ongoing discourse on harnessing technology to better understand and manage pediatric asthma effectively.