Discovering Pediatric Asthma Phenotypes on the Basis of Response to Controller Medication Using Machine Learning.

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.

Objectives

Our objectives were to use our novel machine learning algorithm, predictor pursuit (PP), to discover pediatric asthma phenotypes on the basis of asthma control in response to controller medications, to predict longitudinal asthma control among children with asthma, and to identify features associated with asthma control with

SUBMITTER: Ross MK 

PROVIDER: S-EPMC5822415 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature