Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts-Reference-Cited by-同舟云学术

Automated En Masse Machine Learning Model Generation Shows Comparable Performance as Classic Regression Models for Predicting Delayed Graft Function in Renal Allografts

Published:2021-11-22 Issue:12 Volume:105 Page:2646-2654
ISSN:0041-1337
Container-title:Transplantation
language:en
Short-container-title:

Author:

Jen Kuang-Yu,Albahra Samer,Yen Felicia,Sageshima Junichiro,Chen Ling-Xin,Tran Nam,Rashidi Hooman H.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Transplantation

Reference14 articles.

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3. Prediction, prevention, and management of delayed graft function: where are we now?;Nashan;Clin Transplant,2016

4. A useful scoring system for the prediction and management of delayed graft function following kidney transplantation from cadaveric donors.;Chapal;Kidney Int,2014

5. Nomogram for predicting the likelihood of delayed graft function in adult cadaveric renal transplant recipients.;Irish;J Am Soc Nephrol,2003

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