Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.

ABSTRACT:

Background

Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.

Methods

We included hospitalized COVID-19 pneumonia patients from July to September 2021. K-means clustering, an unsupervised machine learning method, was performed to identify clinical phenotypes based on clinical and laboratory variables collected within 24 hours of admission. Variables were normalized before clustering to ensure equal contribution to the analysis. The optimal number of clusters was determined using the elbow method and Silhouette scores. Cox proportional hazard models were used to compare the risk of intubation and 90-day mortality acr

SUBMITTER: Nalinthasnai N 

PROVIDER: S-EPMC11807335 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature