New York, NY - May 9, 2019 – A new study that analyzed 10 years’ worth of data and hundreds of variables about deceased donor kidneys and the recipients who received them has found that with artificial intelligence (AI)—specifically, training machines to learn from past experience—predictive outcomes are more accurate and will continue to improve over time. Kidneys can then be allocated more efficiently, and with less waste than ever before, according to the author.
About 21,000 kidney transplants are performed each year and the majority of the kidneys donated are from deceased donors. Thousands retrieved each year are never transplanted for a variety of reasons. Kidneys retrieved from deceased donors are currently allocated using the Kidney Donor Risk Index (KDRI) and administered by the United Network for Organ Sharing (UNOS).
“There is insurmountable evidence that we are not taking full advantage of these discarded kidneys,” said lead author and PhD candidate at The University of Iowa Eric Pahl, of OmniLife, Inc. “We are throwing away a lot of kidneys. We need to be better stewards of the gift we have in deceased donor organs.”
Pahl said the study suggests that hundreds of kidneys retrieved from deceased donors and then never transplanted may be saved using AI through machine learning. Further studies are planned, including a clinical trial that is scheduled to begin in August using the machine learning method, Pahl said.
Pahl said that the study looked at hundreds of variables, rather than the 10 variables the current system examines when allocating a kidney. They also looked at characteristics of the recipient as well as the donor kidney and found this added significant predictive value.
“It just makes common sense that we should incorporate variables for the recipient,” Pahl said. “It would seem important for better predictable outcomes.”
Pahl is a Health Informatics Ph.D. student at the University of Iowa and participates in the Interdisciplinary Graduate Program in Informatics (IGPI). With the help of his IGPI mentors, including primary adviser Hans Johnson, Associate Professor of Electrical and Computer Engineering; Informaticist Nick Street, Tippie College of Business; Management Sciences; and Transplant Surgeon Alan Reed, UI Hospitals and Clinics, Pahl focused on the task of using AI and machine learning to compensate for missing data.
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