Novel Machine Learning Identifies 5 Asthma Phenotypes Using Cluster Analysis of Real-World Data.

ABSTRACT:

Background

Asthma classification into different subphenotypes is important to guide personalized therapy and improve outcomes.

Objectives

To further explore asthma heterogeneity through determination of multiple patient groups by using novel machine learning (ML) approaches and large-scale real-world data.

Methods

We used electronic health records of patients with asthma followed at the Cleveland Clinic between 2010 and 2021. We used k-prototype unsupervised ML to develop a clustering model where predictors were age, sex, race, body mass index, prebronchodilator and postbronchodilator spirometry measurements, and the usage of inhaled/systemic steroids. We applied elbow and silhouette plots to select the optimal number of clusters. These clusters were then evaluated th

SUBMITTER: Wu CP 

PROVIDER: S-EPMC11340628 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature