EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics.
allergy
artificial intelligence
asthma
environment
exposome
Journal
Allergy
ISSN: 1398-9995
Titre abrégé: Allergy
Pays: Denmark
ID NLM: 7804028
Informations de publication
Date de publication:
07 2023
07 2023
Historique:
revised:
17
01
2023
received:
20
12
2022
accepted:
01
02
2023
medline:
3
7
2023
pubmed:
7
2
2023
entrez:
6
2
2023
Statut:
ppublish
Résumé
Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large-scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine-learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine-learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1742-1757Informations de copyright
© 2023 European Academy of Allergy and Clinical Immunology and John Wiley & Sons Ltd.
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