Two recent items:
Adapting a Kidney Exchange Algorithm to Align with Human Values
by Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P. Dickerson, Vincent Conitzer
Abstract:
The efficient allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who get what—and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices,and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply.However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments.
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And
How analytics and machine learning can aid organ transplant decisions
by Dimitris Bertsimas and Nikolaos Trichakis
"MIT Sloan and Massachusetts General Hospital have developed an analytics tool to help doctors in deceased-kidney acceptance decisions. The model aims to calculate the probability of a patient being offered a deceased-donor kidney of a certain quality level within a specific time frame (three, six, or 12 months), given their individual characteristics. Using machine learning, it looks at 10 years of data and millions of prior decisions to estimate a patient’s waiting time in the context of a current active organ offer until the time to the next offer for a higher quality kidney."
Adapting a Kidney Exchange Algorithm to Align with Human Values
by Rachel Freedman, Jana Schaich Borg, Walter Sinnott-Armstrong, John P. Dickerson, Vincent Conitzer
Abstract:
The efficient allocation of limited resources is a classical problem in economics and computer science. In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ. Patients and donors in kidney exchanges are prioritized using ad-hoc weights decided on by committee and then fed into an allocation algorithm that determines who get what—and who does not. In this paper, we provide an end-to-end methodology for estimating weights of individual participant profiles in a kidney exchange. We first elicit from human subjects a list of patient attributes they consider acceptable for the purpose of prioritizing patients (e.g., medical characteristics, lifestyle choices,and so on). Then, we ask subjects comparison queries between patient profiles and estimate weights in a principled way from their responses. We show how to use these weights in kidney exchange market clearing algorithms. We then evaluate the impact of the weights in simulations and find that the precise numerical values of the weights we computed matter little, other than the ordering of profiles that they imply.However, compared to not prioritizing patients at all, there is a significant effect, with certain classes of patients being (de)prioritized based on the human-elicited value judgments.
**************
And
How analytics and machine learning can aid organ transplant decisions
by Dimitris Bertsimas and Nikolaos Trichakis
"MIT Sloan and Massachusetts General Hospital have developed an analytics tool to help doctors in deceased-kidney acceptance decisions. The model aims to calculate the probability of a patient being offered a deceased-donor kidney of a certain quality level within a specific time frame (three, six, or 12 months), given their individual characteristics. Using machine learning, it looks at 10 years of data and millions of prior decisions to estimate a patient’s waiting time in the context of a current active organ offer until the time to the next offer for a higher quality kidney."
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