Imperial College London
Portrait Picture

ProfessorIanAdcock

Faculty of Medicine, National Heart & Lung Institute

Professor of Respiratory Cell & Molecular Biology
 
 
 
//

Contact

 

+44 (0)20 7594 7840ian.adcock Website

 
 
//

Location

 

304Guy Scadding BuildingRoyal Brompton Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Shamji:2023:10.1111/all.15667,
author = {Shamji, MH and Ollert, M and Adcock, IM and Bennett, O and Favaro, A and Sarama, R and Riggioni, C and Annesi-Maesano, I and Custovic, A and Fontanella, S and Traidl-Hoffmann, C and Nadeau, K and Cecchi, L and Zemelka-Wiacek, M and Akdis, CA and Jutel, M and Agache, I},
doi = {10.1111/all.15667},
journal = {Allergy},
pages = {1742--1757},
title = {EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics},
url = {http://dx.doi.org/10.1111/all.15667},
volume = {78},
year = {2023}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Shamji,MH
AU  - Ollert,M
AU  - Adcock,IM
AU  - Bennett,O
AU  - Favaro,A
AU  - Sarama,R
AU  - Riggioni,C
AU  - Annesi-Maesano,I
AU  - Custovic,A
AU  - Fontanella,S
AU  - Traidl-Hoffmann,C
AU  - Nadeau,K
AU  - Cecchi,L
AU  - Zemelka-Wiacek,M
AU  - Akdis,CA
AU  - Jutel,M
AU  - Agache,I
DO  - 10.1111/all.15667
EP  - 1757
PY  - 2023///
SN  - 0105-4538
SP  - 1742
TI  - EAACI guidelines on environmental science in allergic diseases and asthma - Leveraging artificial intelligence and machine learning to develop a causality model in exposomics
T2  - Allergy
UR  - http://dx.doi.org/10.1111/all.15667
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000935500700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - https://onlinelibrary.wiley.com/doi/10.1111/all.15667
UR  - http://hdl.handle.net/10044/1/106199
VL  - 78
ER  -
Download