Lupus & antiphospholipid syndrome
Explainable machine learning for predicting infections that require hospitalization in patients with systemic lupus erythematosus
Authors' conclusion
We developed an interpretable ML model as a supporting tool for predicting serious infections in SLE patients.
Study context
- Population
- Patients with systemic lupus erythematosus (SLE), outpatient data at Taichung Veterans General Hospital (2012–2023)
- Participants
- 18,331 patients
- Outcome
- Prediction of infections requiring hospitalization (identified by ICD-9 and ICD-10 codes)
Methodological detailssingle-centre, machine learning prediction model, Lasso regression feature selection, temporal validation
- Single-centre
- Machine learning prediction model
- Lasso regression feature selection
- Temporal validation
Main finding
As reported in the abstract
Prediction of infections requiring hospitalization
| Group | AUROC, testing cohort | AUROC, temporal validation cohort | Specificity | NPV |
|---|---|---|---|---|
| XGBoost | 88% | 84% | 85% | 98% |
XGBoost
AUROC, testing cohort88%
AUROC, temporal validation cohort84%
Specificity85%
NPV98%
Observational data: associations, not effects.
Parts of this Brief are withheld: the automatic checks could not confirm them against the abstract.
Original publication
Explainable machine learning for predicting infections that require hospitalization in patients with systemic lupus erythematosus. Rheumatology (Oxford). Published 16 April 2026. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →