6533b7d4fe1ef96bd1263408
RESEARCH PRODUCT
Machine learning: A modern approach to pediatric asthma
Giovanna CilluffoSalvatore FasolaGiuliana FerranteAmelia LicariGiuseppe Roberto MarsegliaAndrea AlbarelliGian Luigi MarsegliaStefania La Gruttasubject
Phenotypemachine learningchildrenasthma children machine learning phenotypesImmunologyPediatrics Perinatology and Child Healthasthma children machine learning phenotypesphenotypesHumansImmunology and AllergyasthmaChildrespiratory tract diseasesdescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-07-25 |