Optimal Kidney Exchange with Immunosuppressants
Authors: Haris Aziz, Ágnes Cseh, John P. Dickerson, Duncan C. McElfresh21-29
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the utility of immunosuppressants, with computational experiments on simulated kidney exchange graphs generated using data from the United Network for Organ Sharing (UNOS). |
| Researcher Affiliation | Academia | 1 UNSW Sydney and Data61 CSIRO 2 Hasso-Plattner-Institute, University of Potsdam and Institute of Economics, Centre for Economic and Regional Studies 4 University of Maryland |
| Pseudocode | No | The paper describes algorithms and provides an ILP formulation, but it does not include pseudocode or clearly labeled algorithm blocks with structured steps. |
| Open Source Code | No | The paper does not include any statement about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | In this section, we demonstrate the utility of immunosuppressants, with computational experiments on simulated kidney exchange graphs generated using data from the United Network for Organ Sharing (UNOS). |
| Dataset Splits | No | The paper describes generating simulated graphs for experiments and evaluating them, but it does not specify traditional training, validation, and test dataset splits (e.g., percentages, sample counts, or specific named splits) typically used for machine learning models. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU/CPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper mentions Integer Linear Programming (ILP) formulations and cites papers that use specific solvers like CPLEX, but it does not explicitly state which specific software or solver, along with its version number, was used for *their* experiments. |
| Experiment Setup | Yes | For each graph we find the optimal matching by solving ILP (1) with a budget of h {0, . . . , 100} suppressants. [...] All edges have weight 1. [...] we randomly create edges between fraction α [0, 1] of these pairs. |