competing-risks

Competing Risks and Survival Analysis

Finite-Interval Forecasting Engine: Machine learning models for discrete-time survival analysis and multivariate time series forecasting

Implementation of the DeepHit model for survival analysis with competing risks using PyTorch

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.

Competing-risks survival re-examination of the Mayo PBC cirrhosis benchmark (UCI Cirrhosis dataset): a leakage-free, reproducible pipeline benchmarking seven models against the Mayo clinical score.

SLAM-informed fully synthetic survival and competing-risk simulation for dementia institutionalisation prediction

Intuit TechWeek NYC 2026 SMB Underwriting Challenge entry. A selective-labels credit-risk pipeline: one competing-risks hazard spine feeds all four deliverables — timing-integrated NPV decisions, cohort default trajectories, and do() counterfactuals. Validator-clean (RESULT: PASS, 0/0). sklearn-only, deterministic.

Competing-risks survival analysis of 38,201 US interconnection-queue requests (LBNL): who gets built is predictable on day one (AUC 0.72, 3.7x top-decile lift) — who withdraws is a post-entry story.