  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 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.
