Distinguishing Asthma Phenotypes Using Machine Learning Approaches.
File(s)
Author(s)
Howard, R
Rattray, M
Prosperi, M
Custovic, A
Type
Journal Article
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.
Date Issued
2015-07-31
Start Page
38
Journal / Book Title
Curr Allergy Asthma Rep
Volume
15
Issue
7
Copyright Statement
© 2015 The Author. This an open access article published by Springer under the CC BY license
License URL
Identifier
http://www.ncbi.nlm.nih.gov/pubmed/26143394
Subjects
Artificial Intelligence
Asthma
Humans
Models, Biological
Phenotype
Respiratory Sounds
Software
Allergy
Publication Status
Published
Coverage Spatial
United States