WarfaRisk: A Reproducible, Fairness-Aware Machine Learning Pipeline for Warfarin Dose Prediction (Prototype)

Warfarin dosing is complicated by wide inter-patient variability driven by genetic, demographic, and pharmacological factors. Although machine learning approaches to dose prediction have been widely studied, published evaluations are frequently affected by analysis-domain data leakage, small held-out sets, and an absence of calibration and fairness assessment across ancestry subgroups. We describe WarfaRisk, a nine-phase open-source machine learning pipeline for warfarin dose prediction evaluated on two public International Warfarin Pharmacogenetics Consortium (IWPC) cohorts (n=6037 and n=1780, the latter a non-independent secondary consistency check) using fixed patient-level splits and a single documented random seed. The pipeline incorporates leakage-free preprocessing, nested clinical and pharmacogenomic feature ablation across nine model architectures, leave-one-ancestry-out portability assessment with a cross-model robustness check, conformal prediction calibration reported at subgroup resolution, and individual-level SHapley Additive exPlanations (SHAP) feature attribution. The nine-architecture ablation results reported here are from the primary cohort, IWPC-6256; the complete two-cohort comparison is reported in a companion evaluation manuscript. A prototype web-based clinical decision support interface integrates dose recommendation, uncertainty quantification, and SHAP explainability with a Fast Healthcare Interoperability Resources (FHIR) R4 endpoint. In preliminary evaluation on a single fixed held-out split, Model Agnostic Prediction Interval Estimator (MAPIE) cross-conformal (5-fold CV+) prediction achieves coverage close to its nominal target when measured across each cohort as a whole. When this coverage is instead stratified by ancestry, a different pattern emerges: coverage for African-ancestry patients falls well short, a gap the aggregate number hides, plausibly explained by alleles missing from the standard IWPC genotype panel and supported by independent pharmacogenetic literature, though not directly demonstrated in this dataset. These findings indicate that subgroup-level calibration reporting is necessary, not optional, since its absence can let a model appear well-calibrated in aggregate while failing a specific population in practice.