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Does machine learning have a role in the prediction of asthma in children?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.
Research
Protocol for establishing a core outcome set for evaluation in studies of pulmonary exacerbations in people with cystic fibrosisPulmonary exacerbations are associated with increased morbidity and mortality in people with cystic fibrosis (CF). There is no consensus about which outcomes should be evaluated in studies of pulmonary exacerbations or how these outcomes should be measured.
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The role of exome sequencing in childhood interstitial or diffuse lung diseaseChildren’s interstitial and diffuse lung disease (chILD) is a complex heterogeneous group of lung disorders. Gene panel approaches have a reported diagnostic yield of ~ 12%. No data currently exist using trio exome sequencing as the standard diagnostic modality.
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Implementation of on-line training modules in paediatric Aboriginal lung healthAndré Schultz MBChB, PhD, FRACP Program Head, Respiratory Health RFA Program Head, Respiratory Health RFA Prof André Schultz is the Program Head of
Research
Respiratory Health ProgramListed are The Kids Research Institute Australia research teams involved in our Respiratory Health Program. This program sits under the Chronic and Severe Diseases research theme.