  Kidney transplantation can significantly enhance living standards for people
suffering from end-stage renal disease. A significant factor that affects graft
survival time (the time until the transplant fails and the patient requires
another transplant) for kidney transplantation is the compatibility of the
Human Leukocyte Antigens (HLAs) between the donor and recipient. In this paper,
we propose 4 new biologically-relevant feature representations for
incorporating HLA information into machine learning-based survival analysis
algorithms. We evaluate our proposed HLA feature representations on a database
of over 100,000 transplants and find that they improve prediction accuracy by
about 1%, modest at the patient level but potentially significant at a societal
level. Accurate prediction of survival times can improve transplant survival
outcomes, enabling better allocation of donors to recipients and reducing the
number of re-transplants due to graft failure with poorly matched donors.
