A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis.

A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis.

Deciding who should receive a liver transplant (LT) depends on both urgency and utility. Most survival scores are validated through discriminative tests, which compare predicted outcomes between patients. Assessing post-transplant survival utility is not discriminate, but should be "calibrated&...

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Bibliographic Details
Main Authors: Axel Andres, Aldo Montano-Loza, Russell Greiner, Max Uhlich, Ping Jin, Bret Hoehn, David Bigam, James Andrew Mark Shapiro, Norman Mark Kneteman
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5854273?pdf=render

Internet

http://europepmc.org/articles/PMC5854273?pdf=render

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