Using Machine Learning to Compare IRD Organ Transplant Survival to Waiting for a Non-IRD Organ

David Goldsman, Brian Gurbaxani, Pinar Keskinocak, Joel Sokol
Ethan Mark, Kirthana Hampapur

In this project, we have used machine learning to develop a decision support tool for patients on the deceased-donor organ transplant waiting list. The idea is to evaluate the trade-offs in accepting organs that are labelled as 'Increased Risk Donor' (or IRD) organ versus choosing to remain on the waitlist to receive a non-IRD organ. We have built post-transplant survival models to predict the outcomes in both cases and compared them, based on the patient and donor characteristics, to help patients in making more informed decisions. We are working on building a simulation model to evaluate system-wide patient outcomes due to changes such as IRD organ offers being accepted by patients whose survival probability is predicted to be higher when they accept the IRD organ.


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