Does machine learning have a role in the prediction of asthma in children?
Paediatric Respiratory Reviews
School of Science / Centre for Integrative Metabolomics and Computational Biology
Curtin University Research Stipend Scholarship, Australian National Health and Medical Research Council Early Career Fellowship (APP1140312), Medical Research Future Fund Investigator Grant (APP1193796).
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.
Prevention, detection and management of cancer and other chronic diseases