title: Distinguishing Asthma Phenotypes Using Machine Learning Approaches.
abstract: Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as 'asthma endotypes'. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies.
authors.affiliation: Centre for Health Informatics, Institute of Population Health, University of Manchester, Manchester, UK.
authors.forename: Rebecca
authors.initials: R
authors.lastname: Howard
authors.forename: Magnus
authors.initials: M
authors.lastname: Rattray
authors.forename: Mattia
authors.initials: M
authors.lastname: Prosperi
authors.forename: Adnan
authors.initials: A
authors.lastname: Custovic
mesh_terms: D001185:Artificial Intelligence; D001249:Asthma / Q000175:diagnosis* / Q000188:drug therapy; D006801:Humans; D008954:Models, Biological; D010641:Phenotype; D012135:Respiratory Sounds; D012984:Software