Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.
Journal:
Annals of the American Thoracic Society
PMID:
29048949
Abstract
RATIONALE: Pediatric asthma has variable underlying inflammation and symptom control. Approaches to addressing this heterogeneity, such as clustering methods to find phenotypes and predict outcomes, have been investigated. However, clustering based on the relationship between treatment and clinical outcome has not been performed, and machine learning approaches for long-term outcome prediction in pediatric asthma have not been studied in depth.