Mechanisms for Online Organ Matching

Authors: Nicholas Mattei, Abdallah Saffidine, Toby Walsh

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We perform a number of experiments using real world data provided by the Organ and Tissue Authority of Australia. We find that our simple mechanism is more efficient and fairer in practice compared to the other mechanism currently under consideration.
Researcher Affiliation Collaboration Nicholas Mattei IBM T.J. Watson Research Center n.mattei@ibm.com Abdallah Saffidine UNSW Sydney abdallah.saffidine@gmail.com Toby Walsh UNSW Sydney, Data61, TU Berlin toby.walsh@data61.csiro.au
Pseudocode No The mechanisms (MIN and BOX) are described in natural language with bullet points and rules, but not in formal pseudocode blocks or algorithms.
Open Source Code Yes Our simulator, along with one for the US kidney exchange market, is available at www.preflib.org [Mattei and Walsh, 2013].
Open Datasets Yes Public ANZDATA: Long run statistics published by www.anzdata.org.au. ... Research ANZDATA: Detailed data from 2010 2014, enabling us to create more fidelity for our random simulators. This data includes the waiting list at a single point in the year and information on all kidneys donated in 2010-2014.
Dataset Splits No The paper describes using the first four years of simulated data to "burn in" the simulator and reporting statistics on the latter four years, but this is not a traditional train/validation/test split for model training or evaluation in a machine learning context.
Hardware Specification No The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments.
Software Dependencies No The paper mentions that their simulator is available at www.preflib.org but does not specify any software dependencies or their version numbers (e.g., programming languages, libraries, operating systems).
Experiment Setup Yes For our experiments we generate an initial list of patients and kidneys and then simulate the arrivals, departures, and donations by stepping through a simulated 8 years. We repeat this process 1000 times to gain confidence in the statistics we report here [Cohen, 1995]. We use the first four years of data to burn in our simulator, so that the exchange has reached a steady state, and report statistics based on the latter four years of data. The same list of kidneys and patients (their order of arrival) are used for all of the treatments (both mechanisms and both state and federal exchanges).