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

Mohamed H. Shamji*, Markus Ollert, Ian M. Adcock, Oscar Bennett, Alberto Favaro, Roudin Sarama, Carmen Riggioni, Isabella Annesi-Maesano, Adnan Custovic, Sara Fontanella, Claudia Traidl-Hoffmann, Kari Nadeau, Lorenzo Cecchi, Magdalena Zemelka-Wiacek, Cezmi A. Akdis, Marek Jutel*, Ioana Agache*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

17 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1742-1757
Number of pages16
JournalAllergy: European Journal of Allergy and Clinical Immunology
Volume78
Issue number7
Early online date5 Feb 2023
DOIs
Publication statusPublished - Jul 2023

Keywords

  • allergy
  • artificial intelligence
  • asthma
  • environment
  • exposome

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