Assessing clusters of comorbidities in rheumatoid arthritis: a machine learning approach.

ABSTRACT:

Background

Comorbid conditions are very common in rheumatoid arthritis (RA) and several prior studies have clustered them using machine learning (ML). We applied various ML algorithms to compare the clusters of comorbidities derived and to assess the value of the clusters for predicting future clinical outcomes.

Methods

A large US-based RA registry, CorEvitas, was used to identify patients for the analysis. We assessed the presence of 24 comorbidities, and ML was used to derive clusters of patients with given comorbidities. K-mode, K-mean, regression-based, and hierarchical clustering were used. To assess the value of these clusters, we compared clusters across different ML algorithms in clinical outcome models predicting clinical disease activity index (CDAI) and health ass

SUBMITTER: Solomon DH 

PROVIDER: S-EPMC10664370 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature