EAACI guidelines on environmental science in allergic diseases and asthma – Leveraging artificial intelligence and machine learning to develop a causality model in exposomics

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...

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Bibliographic Details
Published in:	Allergy (Copenhagen) Vol. 78; no. 7; pp. 1742 - 1757
Main Authors:	Shamji, Mohamed H., Ollert, Markus, Adcock, Ian M., Bennett, Oscar, Favaro, Alberto, Sarama, Roudin, Riggioni, Carmen, Annesi‐Maesano, Isabella, Custovic, Adnan, Fontanella, Sara, Traidl‐Hoffmann, Claudia, Nadeau, Kari, Cecchi, Lorenzo, Zemelka‐Wiacek, Magdalena, Akdis, Cezmi A., Jutel, Marek, Agache, Ioana
Format:	Journal Article
Language:	English
Published:	Denmark Blackwell Publishing Ltd 01.07.2023 Wiley
Series:	The EAACI Hybrid Congress 2023
Subjects:	Allergic diseases Allergology allergy Artificial Intelligence Asthma Asthma - diagnosis Asthma - epidemiology Asthma - etiology environment Environmental Science exposome health services Humans Hypersensitivity - diagnosis Hypersensitivity - epidemiology Hypersensitivity - etiology Immune system Immunology Learning algorithms Life Sciences Machine Learning Precision medicine Public health allergy environment asthma exposome artificial intelligence Allergy Environment Exposome Artificial intelligence Asthma
ISSN:	0105-4538, 1398-9995, 1398-9995
Online Access:	Get full text
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