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Updated: Jul 19, 2025

The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice

The bm12 Inducible Model of Systemic Lupus Erythematosus SLE in C57BL/6 Mice

Published on: November 1, 2015

Exploring machine learning methods for predicting systemic lupus erythematosus with herpes.

Da-Cheng Wang¹, Yang-Yang Tang¹, Cheng-Song He²

¹Department of Evidence-Based Medicine, Southwest Medical University, Luzhou, Sichuan, China.

International Journal of Rheumatic Diseases

|August 14, 2023

Summary

This summary is machine-generated.

Machine learning models can predict herpes in systemic lupus erythematosus (SLE) patients. Key predictors include white blood cell count, age, and immunoglobulin levels, aiding early clinical identification.

Keywords:

herpes zoster machine learning prediction model systemic lupus erythematosus

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Area of Science:

Computational biology
Immunology
Dermatology

Background:

Machine learning is prevalent in disease prediction using demographic and serological data.
Systemic lupus erythematosus (SLE) patients are susceptible to various infections, including herpes.

Purpose of the Study:

To evaluate the efficacy of machine learning in predicting herpes occurrence among SLE patients.
To identify key predictive factors for herpes complications in SLE.

Main Methods:

A cohort of 286 SLE patients (86 with herpes, 200 without) was analyzed.
Demographic and serological data were used to train and test machine learning models.
Feature importance and model performance were assessed using random forest, logistic regression, and decision tree analyses.

Main Results:

Key features for prediction included basophil, monocyte, white blood cell counts, age, immunoglobulin E, SLE Disease Activity Index, complement 4, neutrophil, and immunoglobulin G.
The random forest model demonstrated strong predictive performance.
Logistic and decision tree models offered practical clinical decision-making benefits.

Conclusions:

Machine learning, particularly the random forest model, shows promise for early identification of SLE patients at risk of herpes complications.
This approach can assist clinicians in proactive patient management and intervention.