Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Toward a Well-Calibrated Discrimination via Survival Outcome-Aware Contrastive Learning
Authors: Dongjoon Lee, Hyeryn Park, Changhee Lee
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on multiple real-world clinical datasets demonstrate that our method outperforms state-of-the-art deep survival models in both discrimination and calibration. |
| Researcher Affiliation | Academia | Dongjoon Lee Chung-Ang University EMAIL Hyeryn Park Chung-Ang University EMAIL Changhee Lee Korea University EMAIL |
| Pseudocode | Yes | Please find the pseudo-code of Con Surv in H. |
| Open Source Code | Yes | 2Source code for Con Surv is available in https://github.com/dongzza97/Con Surv |
| Open Datasets | Yes | Datasets. We compare our proposed method and the benchmarks with the following four commonly used real-world clinical datasets: METABRIC, NWTCO, GBSG, FLCHAIN, SUPPORT, and SEER. For detailed descriptions of these datasets, please refer to D.1. |
| Dataset Splits | Yes | We split the data into train, test, and validation sets with a ratio of 0.64:0.20:0.16, and then apply min-max normalization to the input features. |
| Hardware Specification | Yes | The specification of the machine is CPU: INTEL XEON Gold 6240R, GPU: NVIDIA RTX A6000 |
| Software Dependencies | No | The paper mentions general software components and links to GitHub repositories for benchmark models (e.g., DeepHit, DRSA, DCS, X-CAL), but it does not specify versions for its own core software dependencies like Python, PyTorch, or other libraries used in its implementation. |
| Experiment Setup | Yes | We perform a random search for hyperparameter optimization including the batch size, hidden dimension, depth, learning rates, corruption rates, σ, α, and ν on the validation set and choose the settings with the best performance for Con Surv on each dataset. Table 12 describes the model specifications for the evaluated datasets. |