Does machine learning have a role in the prediction of asthma in children?. (March 2022)

• Record Type: Journal Article
• Authors: Patel, Dimpalben; Hall, Graham L.; Broadhurst, David; Smith, Anne; Schultz, André; Foong, Rachel E.
• Is Part Of: Paediatric respiratory reviews. Volume 41(2022)
• Page Start: 51
• Page End: 60
• Publication Date: 2022-03
• Subjects: AUC Area Under receiver operating characteristic Curve -- ML Machine Learning -- MLP Multi-Layer Perception -- PROBAST Prediction model Risk Of Bias Assessment Tool; Asthma -- Machine learning -- Prediction -- Children; Pediatric respiratory diseases -- Periodicals; Respiratory organs -- Diseases -- Periodicals; Respiratory Tract Diseases -- Periodicals; Respiratory Therapy -- Periodicals; Child -- Periodicals; Appareil respiratoire -- Infections, chez l'enfant -- Périodiques; Appareil respiratoire -- Maladies, chez l'enfant -- Périodiques; 618.922
• DOI: 10.1016/j.prrv.2021.06.002
• Languages: English
• ISSNs: 1526-0542

Abstract

Abstract: Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. Machine learning is an approach that may improve on conventional models by finding patterns and trends from large and complex datasets. Thus far, few studies have utilized machine learning to predict asthma in children. This review aims to critically assess these studies, describe their limitations, and discuss future directions to move from proof-of-concept to clinical application.