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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Summary: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.
Bibliography:Mohamed H. Shamji, Markus Ollert and Ian M. Adcock are first co‐authorship.
Cezmi A. Akdisi, Marek Jutel and Ioana Agache are Joint senior co‐authorship.
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ISSN:0105-4538
1398-9995
1398-9995
DOI:10.1111/all.15667