SAG-RS: Trajectory-Aware Probability Stacking with Contextual-Bandit Reliability Gating for ICD-Audited Diabetes Phenotype Identification in MIMIC-IV

Background: Identifying diabetes phenotypes in electronic health records is challenging when repeated admissions and diagnosis-derived variables can bias evaluation. Methods: We reconstructed an ICD-audited diabetes phenotype from MIMIC-IV v3.1 and compared LightGBM with trajectory-aware probability stacking and a conservative contextual-bandit reliability gate. Patient-level separation, out-of-fold feature generation, independent gate development, and a common upstream Base model were used. Results: On the primary held-out split (51,400 records), Full SAG-RS achieved accuracy 0.926654, F1 0.889579, AUC 0.961288, and Brier 0.059649. Across five patient-level splits, mean accuracy was 0.928021 ± 0.002217. The gate improved primary-split accuracy over Stack-only by 0.000233 (95% paired confidence interval −0.000078 to 0.000548); net corrections were positive in three of five splits. Conclusions: Trajectory-aware stacking provided most of the gain over admission-level LightGBM. The gate offered limited, variable correction rather than a demonstrated independent performance gain. Findings are restricted to retrospective internal validation; external validation and full-pipeline replication remain outstanding.