Peter Hall, Bego Ña, B Sierra, Colin Mclean, Sarah Friedrich-Welz, Catalina Vallejos
We lifted 9 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| BBolosSierra/CompRisksBenchmark | canonical | 8 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| FC_Net | Ran | BBolosSierra/CompRisksBenchmark/python/utils_DeepHit.py pointer only (licence: NONE) · get_code("bebfc84fd2799205") |
| c_index | Ran | BBolosSierra/CompRisksBenchmark/python/metrics.py pointer only (licence: NONE) · get_code("c22b3a72aa73bd3d") |
| create_concat_state | Ran | BBolosSierra/CompRisksBenchmark/python/utils_DeepHit.py pointer only (licence: NONE) · get_code("774278cf912b22e9") |
| div | Ran | BBolosSierra/CompRisksBenchmark/python/class_DeepHit.py pointer only (licence: NONE) · get_code("3c4f496c3ef095d8") |
| load_manifest_times | Ran | BBolosSierra/CompRisksBenchmark/python/utils.py pointer only (licence: NONE) · get_code("25d0b686c52c06d5") |
| load_outer_fold | Ran | BBolosSierra/CompRisksBenchmark/python/utils.py pointer only (licence: NONE) · get_code("ed0b588c071c4743") |
| load_outer_fold | Ran | BBolosSierra/CompRisksBenchmark/python/utils.py pointer only (licence: NONE) · get_code("a116780074798300") |
| log | Ran | BBolosSierra/CompRisksBenchmark/python/class_DeepHit.py pointer only (licence: NONE) · get_code("ab04280cb9105858") |
| create_rnn_cell | Not yet run | BBolosSierra/CompRisksBenchmark/python/utils_DeepHit.py pointer only (licence: NONE) · get_code("88a0f730092b409d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.e., cancer death) precludes the occurrence of other events (i.e., cardiovascular disease death). Despite these methodological advances, their systematic evaluation and adoption are limited by the lack of comprehensive, reproducible and extensible benchmarking frameworks. We developed an open-source benchmarking framework for competing risks models that enables their systematic comparison across multiple datasets under different aspects of performance; calibration, discrimination, overall prediction error and clinical utility. We additionally introduce an extension of SHAP for competing risks, allowing model-agnostic interpretability of covariates contributions over time. All our code is publicly available via GitHub: https://github.com/BBolosSierra/CompRisksBenchmark.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2608.00271")
get_code_for_paper("2608.00271")
have("2608.00271")
Connect an agent — have() is free.