Objective Renal transplantation has dramatically improved the survival rate of hemodialysis patients. However, with a growing proportion of marginal organs and improved immunosuppression, it is necessary to verify that the established allocation system, mostly based on human leukocyte antigen matching, still meets today's needs. The authors turn to machine-learning techniques to predict, from donorerecipient data, the estimated glomerular filtration rate (eGFR) of the recipient 1 year after transplantation. Design The patient's eGFR was predicted using donorerecipient characteristics available at the time of transplantation. Donors' data were obtained from Eurotransplant's database, while recipients' details were retrieved from Charité Campus Virchow-Klinikum's database. A total of 707 renal transplantations from cadaveric donors were included. Measurements Two separate datasets were created, taking features with <10% missing values for one and <50% missing values for the other. Four established regressors were run on both datasets, with and without feature selection.
For example, REF constructed a series of classifiers to predict the estimated glomerular filtration rate (eGFR) of kidney transplant patients, with the help of 56 selected features from the donor and recipient.
Predicting the outcome of renal transplantation
Journal of the American Medical Informatics Association : JAMIA
Journal of the American Medical Informatics Association : JAMIA
Computer Science Medicine