Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks
Authors:
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real data show that our model outperforms the state-of-the-art survival models. |
| Researcher Affiliation | Academia | Ahmed M. Alaa Electrical Engineering Department University of California, Los Angeles ahmedmalaa@ucla.edu Mihaela van der Schaar Department of Engineering Science University of Oxford mihaela.vanderschaar@eng.ox.ac.uk |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about the availability of its source code. |
| Open Datasets | Yes | We conducted experiments on a real-world patient cohort extracted from a publicly accessible dataset provided by the Surveillance, Epidemiology, and End Results Program (SEER). https://seer.cancer.gov/causespecific/ |
| Dataset Splits | Yes | We divide D into 500 subjects for training and 500 subjects for out-of-sample testing. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'R libraries cmprsk and survival', 'R-package threg', and 'R-package pec', and the 'ADAM algorithm', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We run the algorithm in Section 4 with Q = 3 outputs for the first layer of the DMGP, and we use the default settings prescribed in [18] for the ADAM algorithm. |