Lupus & antiphospholipid syndrome
Application of machine learning in assessing disease activity in SLE
Authors' conclusion
This pioneering machine learning model, using objective laboratory indicators, enhances clinical feasibility and provides a novel method for assessing SLE disease activity
Study context
- Population
- Patients with SLE; cohort 1 training set, cohort 2 external validation
- Participants
- 319 patients2 cohorts
- Outcome
- Distinguishing active SLE (SLE disease activity assessment)
Methodological detailsmachine learning, external validation
- Machine learning
- External validation
Performance
As reported in the abstract
Distinguishing active SLE
| Test or model | AUC | Accuracy | Sensitivity | Specificity | F1 score |
|---|---|---|---|---|---|
| XGBoost | 0.934 | 0.925 | 0.969 | 0.750 | 0.954 |
XGBoost
AUC0.934
Accuracy0.925
Sensitivity0.969
Specificity0.750
F1 score0.954
Original publication
Application of machine learning in assessing disease activity in SLE. Lupus Sci Med. Published 8 April 2025. doi:10.
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