Multitask Boosting for Survival Analysis with Competing Risks
Authors: Alexis Bellot, Mihaela van der Schaar
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of our algorithm on synthetic and real data. ... 5 Experiments ... 5.2 Synthetic Studies ... 5.3 Real data studies: SEER |
| Researcher Affiliation | Academia | Alexis Bellot University of Oxford Oxford, United Kingdom alexis.bellot@eng.ox.ac.uk Mihaela van der Schaar University of Oxford and The Alan Turing Institute London, United Kingdom mschaar@turing.ac.uk |
| Pseudocode | Yes | Algorithm 1 Multitask Boosting |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a link or explicit statement of code release) for the source code. |
| Open Datasets | Yes | We investigate a patient population extracted from the Surveillance, Epidemiology, and End Results (SEER) repository similarly to [2]. ... We generate 5 data sets (by sampling variables and parameters randomly) of 1000 instances for each individual scenario and set a random 50% of the population to be uniformly censored. |
| Dataset Splits | Yes | Deep Learning architecture (Deep Hit) [18] with hyperparameters optimized with a validation set. ... these are averages over 4 fold cross-validation estimates and confidence bands are standard deviations. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of statistical methods and models but does not specify any software dependencies with version numbers (e.g., specific Python libraries or statistical packages with their versions). |
| Experiment Setup | Yes | On all experiments we train SMTBoost with a tree-depth of 3 and 250 boosting iterations, our default parameter settings. |