Gradient Regularized V-Learning for Dynamic Treatment Regimes
Authors: Yao Zhang, Mihaela van der Schaar
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using multiple simulation studies and one real-world medical dataset, we demonstrate that our method is superior in DTR evaluation and learning, thereby providing improved treatment options over time for patients. |
| Researcher Affiliation | Academia | Yao Zhang University of Cambridge yz555@cam.ac.uk Mihaela van der Schaar University of Cambridge University of California, Los Angeles The Alan Turing Institute mv472@cam.ac.uk |
| Pseudocode | No | The paper describes the algorithms (GRV-B, GRV-S) in paragraph text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is provided at: https://bitbucket.org/mvdschaar/mlforhealthlabpub. |
| Open Datasets | Yes | We also demonstrate the application of our method on a real-world dataset extracted from the Medical Information Mart for Intensive Care (MIMIC) database [22]." and "[22] Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. Mimic-iii, a freely accessible critical care database. Scientiļ¬c data, 3:160035, 2016. |
| Dataset Splits | No | The paper mentions 'training sample size at 1000, 5000 and 10000' and 'a large testing dataset with 20,000 individuals', but does not provide specific details on validation splits or percentages for training/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of neural network models but does not provide specific software dependencies or their version numbers (e.g., library names with versions). |
| Experiment Setup | No | The paper states that 'The simulation and implementation details of GRV and benchmark methods can be found in Appendix B,' but Appendix B is not provided in the given text. The main text does not contain specific experimental setup details like hyperparameter values or training configurations. |