Machine learning: A modern approach to pediatric asthma.
Giovanna CilluffoSalvatore FasolaGiuliana FerranteGian Luigi MarsegliaGiuseppe Roberto MarsegliaAndrea AlbarelliGian Luigi MarsegliaStefania La GruttaPublished in: Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology (2022)
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.
Keyphrases
- chronic obstructive pulmonary disease
- machine learning
- lung function
- allergic rhinitis
- healthcare
- clinical practice
- big data
- cystic fibrosis
- artificial intelligence
- electronic health record
- deep learning
- air pollution
- risk assessment
- high resolution
- mass spectrometry
- social media
- quantum dots
- human health
- health insurance
- health information
- data analysis
- affordable care act