So far, we have created 2 deep learning models that are focused on the OPO perspective:
- Deceased Donor Kidney Assessment – which evaluates the likelihood that a donated kidney will be transplanted based on up to 18 characteristics (including biopsy information, if available). This can help OPOs determine whether a kidney is hard-to-place based on historical behavior. You can play with this model here: https://ddoa.mst.hekademeia.org/#/
- Final Acceptance – which improves the estimate of the likelihood of transplant by incorporating recipient characteristics. This can be used to make an estimate for each recipient on the Match Run. For hard-to-place kidneys, this can help determine where to go for expedited placement. OPOs could save time by not sending offers that are very unlikely to be accepted.
Both models are trained from the OPTN Deceased Donor Dataset. We are planning to test the impact of these models using UNOS’s SimUNet, which is a research platform that is currently being expanded to include the OPO perspective as part of this project. To date, SimUNet studies have only focused on transplant surgeon decision-making.
To develop models that support transplant surgeons, we believe a more tailored approach is needed. Professor Cihan Dagli and Rachel Dzieran are working on a new model called Transplant Surgeon Fuzzy Associate Memory (TSFAM). The intent is for the model to use deep learning network model interfaces to capture individualized transplant surgeon practices and assessments through fuzzy associative memory. Fuzzy logic accounts for imperfect data and ambiguity, which is more consistent with how humans make decisions. We are identifying decision rules used by an individual transplant surgeon and then tailoring the AI-based decision-making model to support individual decision-making. Case studies are currently being reviewed for building the structure to collect the individualized transplant surgeon policies. The primary goal of this work is to support transplant surgeons by using their own policies when assessing deceased donor organs. Dr. Dagli has two new PhD students joining in Fall 2024 to continue to develop this new model.
Contributor: Rachel Dzieran (S&T), Cihan Dagli (S&T)