Towards Precise and Equitable Organ Allocation in Transplant with Machine Learning

Organ transplant has become an important therapy for many end-stage and severe diseases, e.g., hepatic failure. There is an essential but challenging step in organ transplant which is organ allocation. However, the number of donor organs is scarce compared to the patients who need them. So the allocation of this kind of scarce medical resource remains a challenge. The allocation of organs needs to consider both precision and fairness, which is very hard to balance for the human expert and traditional allocation methods. My Ph.D. research aims to tackle this problem from the computational perspective, especially with machine learning methods.