A fair machine learning model to predict flares of systemic lupus erythematosus.

Abstract

Objective: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that disproportionately affects women and racial/ethnic minority groups. Predicting disease flares is essential for improving patient outcomes, yet few studies integrate both clinical and social determinants of health (SDoH). We therefore developed FLAME (FLAre Machine learning prediction of SLE), a machine learning pipeline that uses electronic health records (EHRs) and contextual-level SDoH to predict 3-month flare risk, emphasizing explainability and fairness.

Materials and methods: We conducted a retrospective cohort study of 28 433 patients with SLE from the University of Florida Health (2011-2022), linked to 675 contextual-level SDoH variables. We used XGBoost and logistic regression models to predict 3-month flare risk, evaluating model performance using the area under the receiver operating characteristic (AUROC). We applied SHapley Additive exPlanations (SHAP) values and causal structure learning to identify key predictors. Fairness was assessed using the equality of opportunity metric, measured by the false-negative rate across racial/ethnic groups.

Results: The FLAME model, incorporating clinical and contextual-level SDoH, achieved an AUROC of 0.66. The clinical-only model performed slightly better (AUROC of 0.67), while the SDoH-only model had lower performance (AUROC of 0.54). SHAP analysis identified headache, organic brain syndrome, and pyuria as key predictors. Causal learning revealed interactions between clinical factors and contextual-level SDoH. Fairness assessments showed no significant biases across groups.

Discussion: FLAME offers a fair and interpretable approach to predicting SLE flares, providing meaningful insights that may guide future clinical interventions.

Conclusions: FLAME shows promise as an EHR-based tool to support personalized, equitable, and holistic SLE care.