Transplantation: Neural networks for predicting graft survival

Bruce Kaplan; Jesse Schold

doi:10.1038/nrneph.2009.24

Transplantation: Neural networks for predicting graft survival

Bruce Kaplan, Jesse Schold

Research output: Contribution to journal › Short survey › peer-review

17 Scopus citations

Abstract

Predicting outcomes of renal transplant recipients using standard statistical techniques is difficult. novel approaches such as the use of artificial neural networks might improve the precision and accuracy in this area of medicine in which numerous and complex events contribute to outcomes.

Original language	English (US)
Pages (from-to)	190-192
Number of pages	3
Journal	Nature Reviews Nephrology
Volume	5
Issue number	4
DOIs	https://doi.org/10.1038/nrneph.2009.24
State	Published - Apr 1 2009

ASJC Scopus subject areas

Nephrology

Access to Document

10.1038/nrneph.2009.24

Cite this

@article{73038cf164df4837aeb9b0eb2812fc13,

title = "Transplantation: Neural networks for predicting graft survival",

abstract = "Predicting outcomes of renal transplant recipients using standard statistical techniques is difficult. novel approaches such as the use of artificial neural networks might improve the precision and accuracy in this area of medicine in which numerous and complex events contribute to outcomes.",

author = "Bruce Kaplan and Jesse Schold",

year = "2009",

month = apr,

day = "1",

doi = "10.1038/nrneph.2009.24",

language = "English (US)",

volume = "5",

pages = "190--192",

journal = "Nature Reviews Nephrology",

issn = "1759-5061",

publisher = "Nature Publishing Group",

number = "4",

}

TY - JOUR

T1 - Transplantation

T2 - Neural networks for predicting graft survival

AU - Kaplan, Bruce

AU - Schold, Jesse

PY - 2009/4/1

Y1 - 2009/4/1

N2 - Predicting outcomes of renal transplant recipients using standard statistical techniques is difficult. novel approaches such as the use of artificial neural networks might improve the precision and accuracy in this area of medicine in which numerous and complex events contribute to outcomes.

AB - Predicting outcomes of renal transplant recipients using standard statistical techniques is difficult. novel approaches such as the use of artificial neural networks might improve the precision and accuracy in this area of medicine in which numerous and complex events contribute to outcomes.

UR - http://www.scopus.com/inward/record.url?scp=67049165542&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67049165542&partnerID=8YFLogxK

U2 - 10.1038/nrneph.2009.24

DO - 10.1038/nrneph.2009.24

M3 - Short survey

C2 - 19322183

AN - SCOPUS:67049165542

SN - 1759-5061

VL - 5

SP - 190

EP - 192

JO - Nature Reviews Nephrology

JF - Nature Reviews Nephrology

IS - 4

ER -

Transplantation: Neural networks for predicting graft survival

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this