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Review
. 2022 Jan;33 Suppl 27(Suppl 27):34-37.
doi: 10.1111/pai.13624.

Machine learning: A modern approach to pediatric asthma

Giovanna Cilluffo  1 , Salvatore Fasola  1 , Giuliana Ferrante  2 , Amelia Licari  3   4 , Giuseppe Roberto Marseglia  5 , Andrea Albarelli  5 , Gian Luigi Marseglia  3   4 , Stefania La Grutta  1
Affiliations
Review

Machine learning: A modern approach to pediatric asthma

Giovanna Cilluffo et al. Pediatr Allergy Immunol. 2022 Jan.

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.

Keywords: asthma; children; machine learning; phenotypes.

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Conflict of interest statement

Authors declared they have no conflict of interests.

Figures

FIGURE 1
FIGURE 1
Word cloud analysis using the title of articles published in the last five years. The list of publications was obtained using the following search strategy (PUBMED): machine learning AND asthma AND children. The pre‐processing procedures applied were as follows: (1) removing words in the search strategy, non‐English words, or common words that do not provide information; (2) changing words into lower case, and (3) removing punctuation and white spaces. The size of the words is proportional to the observed frequency
See this image and copyright information in PMC

References

    1. Deliu M, Yavuz TS, Sperrin M, et al. Features of asthma which provide meaningful insights for understanding the disease heterogeneity. Clin Exp Allergy. 2018;48(1):39‐47. - PMC - PubMed
    1. Su MW, Lin WC, Tsai CH, et al. Childhood asthma clusters reveal neutrophil‐predominant phenotype with distinct gene expression. Allergy. 2018;73(10):2024‐2032. - PubMed
    1. Fitzpatrick AM, Bacharier LB, Jackson DJ, et al. Heterogeneity of mild to moderate persistent asthma in children: confirmation by latent class analysis and association with 1‐year outcomes. J Allergy Clin Immunol Pract. 2020;8(8):2617‐2627.e4. - PMC - PubMed
    1. Fitzpatrick AM, Teague WG, Meyers DA, et al. Heterogeneity of severe asthma in childhood: confirmation by cluster analysis of children in the National Institutes of Health/National Heart, Lung, and Blood Institute Severe Asthma Research Program. J Allergy Clin Immunol. 2011;127(2):382‐389.e1‐13. - PMC - PubMed
    1. Just J, Gouvis‐Echraghi R, Rouve S, Wanin S, Moreau D, Annesi‐Maesano I. Two novel, severe asthma phenotypes identified during childhood using a clustering approach. Eur Respir J. 2012;40(1):55‐60. - PubMed