In Allergy ; h5-index 71.0
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 datasets towards 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 fully representative exposome. We also discuss the promise of artificial intelligence in the personalized medicine and the methodological approaches to healthcare with the final ai to improve the public health.
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
2023-Feb-05
allergy, artificial intelligence, asthma, environment, exposome, machine learning
