Scalable, scikit-learn-compatible competing-risks survival analysis in pure Python — CR random survival forest, Fine-Gray, cause-specific Cox, Aalen-Johansen CIF, Gray's test, and exact TreeSHAP. 10–22× faster than randomForestSRC on real EHR and 16.6–544× vs scikit-survival (n=5k→50k); scales to n=10⁶ in ~1 min.

Deep learning prognostic index (DPI) and time-dependent SurvSHAP(t) interpretability for glioblastoma survival — code scaffold for Lee et al., Radiol Artif Intell 2026 (doi:10.1148/ryai.250675)

Survival models and ML for Korean corporate credit risk assessment

Time-to-event customer churn forecasting pipeline using survival analysis to optimize $3.38M in at-risk retail revenue.

Breast cancer survival analysis using Kaplan-Meier curves, Cox proportional hazards models, and Random Survival Forests.

End-to-end oncology survival analytics platform featuring clinical trial simulation, real-world evidence modeling, survival prediction, validation frameworks, FastAPI deployment, and agent-oriented clinical workflows.