Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations.

ABSTRACT:

Objectives

Systemic lupus erythematosus (SLE) is a heterogeneous disease characterized by disease flares which can require hospitalization. Our objective was to apply machine learning methods to predict hospitalizations for SLE from electronic health record (EHR) data.

Methods

We identified patients with SLE in a longitudinal EHR-based cohort with ≥2 outpatient rheumatology visits between 2012 and 2019. We applied multiple machine learning methods to predict hospitalizations with a primary diagnosis code for SLE, including decision tree, random forest, naive Bayes, logistic regression, and an ensemble method. Candidate predictors were derived from structured EHR features, including demographics, laboratory tests, medications, ICD-9/10 codes for SLE manifestations, and health

SUBMITTER: Jorge AM 

PROVIDER: S-EPMC9547899 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature