Related Experiment Video
Updated: Aug 5, 2026
Isolation and Quantification of Epstein-Barr Virus from the P3HR1 Cell Line
Published on: September 28, 2022
Machine learning models identify prognostic factors in systemic lupus erythematosus patients with epstein-barr virus
Mengyuan Fang1, Mingyu Huang2, Feiping Ge1
1Department of Rheumatology, The First Affiliated Hospital of Wenzhou Medical University, Zhejiang, China.
Objective:
To identify poor prognostic factors in Epstein-Barr virus (EBV)-positive systemic lupus erythematosus (SLE) using interpretable machine-learning (ML) models.
Method:
We retrospectively analysed 217 EBV-positive SLE in-patients (2018-2022). Clinical and laboratory variables were compared between favorable and poor prognosis groups. Seven ML algorithms-logistic regression, support vector machine, naïve Bayes, random forest (RF), gradient boosting machine (GBM), artificial neural network, and AdaBoost-were trained with a 70/30 train-test split. Recursive feature elimination with cross-validation selected the optimal predictor set. SHAP (Shapley additive explanations) illustrated feature importance.
Results:
Six clinical features (SLEDAI-2 K, lupus nephritis, splenomegaly, arthritis, rash, fever) and six laboratory indicators (haemoglobin, 24-h urine protein, serum uric acid, EBNA-IgG, AST, CD3 + T-cell percentage) constituted the final model inputs. The RF model performed best for clinical variables (AUC 0.71; F1 0.55); GBM performed best for laboratory variables (AUC 0.56; F1 0.30). SHAP confirmed SLEDAI-2 K, lupus nephritis, and haemoglobin as the most influential predictors.
Conclusion:
Interpretable ML models highlight disease activity, renal involvement, haematological status, and EBV serology as important model-selected features associated with poor prognosis in EBV-positive SLE. These findings provide exploratory insights into potential prognostic factors. Key Points • Seven machine learning models were systematically compared for prognostic risk prediction in SLE patients with Epstein-Barr virus infection. • Random Forest showed the best performance for clinical indicators, whereas Gradient Boosting Machine performed best for laboratory indicators. • Both clinical and laboratory variables contributed to exploratory risk stratification, and hemoglobin was identified as a model-selected laboratory feature associated with poor-prognosis predictions. • A prognostic risk detection model integrating clinical and laboratory indicators was established for SLE patients with Epstein-Barr virus infection.
