Adversarial Time-to-Event Modeling
Authors: Paidamoyo Chapfuwa, Chenyang Tao, Chunyuan Li, Courtney Page, Benjamin Goldstein, Lawrence Carin Duke, Ricardo Henao
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our model on both benchmark and real datasets, demonstrating that the proposed formulation yields significant performance gains relative to a parametric alternative, which we also propose. and Our model is evaluated on 4 diverse datasets: i) FLCHAIN: a public dataset... ii) SUPPORT: a public dataset... iii) SEER: a public dataset... iv) EHR: a large study from Duke University Health System... and Table 2. Median relative absolute errors (as percentages of tmax), on non-censored data. |
| Researcher Affiliation | Academia | Paidamoyo Chapfuwa 1 Chenyang Tao 1 Chunyuan Li 1 Courtney Page 1 Benjamin Goldstein 1 Lawrence Carin 1 Ricardo Henao 1 1Duke University. Correspondence to: Paidamoyo Chapfuwa <paidamoyo.chapfuwa@duke.edu>. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm block was found in the paper. |
| Open Source Code | Yes | Tensor Flow code to replicate experiments can be found at https://github.com/ paidamoyo/adversarial_time_to_event. |
| Open Datasets | Yes | Datasets Our model is evaluated on 4 diverse datasets: i) FLCHAIN: a public dataset introduced in a study to determine whether non-clonal serum immunoglobin free light chains are predictive of survival time (Dispenzieri et al., 2012). ii) SUPPORT: a public dataset introduced in a survival time study of seriously-ill hospitalized adults (Knaus et al., 1995). iii) SEER: a public dataset provided by the Surveillance, Epidemiology, and End Results Program. See (Ries et al., 2007) for details concerning the definition of the 10-year follow-up breast cancer subcohort used in our experiments. |
| Dataset Splits | Yes | To avoid bias due to multiple encounters per patient, we split the training, validation and test sets so that a given patient can only be in one of the sets. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Tensor Flow code' but does not specify its version number or any other software dependencies with their versions. |
| Experiment Setup | No | The paper states 'Detailed network architectures, optimization parameters and initialization settings are in the Supplementary Material' but does not provide these specific details within the main text. |