Machine learning: A modern approach to pediatric asthma
Giovanna Cilluffo, Salvatore Fasola, Giuliana Ferrante, Amelia Licari, Giuseppe Roberto Marseglia, Andrea Albarelli, Gian Luigi Marseglia, Stefania La Grutta
Abstract
Among modern methods of statistical and computational analysis, the application of machine learning (ML) to healthcare data has been gaining recognition in helping us understand the heterogeneity of asthma and predicting its progression. In pediatric research, ML approaches may provide rapid advances in uncovering asthma phenotypes with potential translational impact in clinical practice. Also, several accurate models to predict asthma and its progression have been developed using ML. Here, we provide a brief overview of ML approaches recently proposed to characterize pediatric asthma.
Topics & Concepts
MedicineAsthmaMachine learningData scienceClinical PracticeArtificial intelligenceTranslational researchIntensive care medicinePhysical therapyImmunologyComputer sciencePathologyAsthma and respiratory diseasesInhalation and Respiratory Drug DeliveryChronic Obstructive Pulmonary Disease (COPD) Research