Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept-Reference-Cited by-同舟云学术

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Big Data, Predictive Analytics, and Quality Improvement in Kidney Transplantation: A Proof of Concept

Published:2017-01-04 Issue:3 Volume:17 Page:671-681
ISSN:1600-6135
Container-title:American Journal of Transplantation
language:en
Short-container-title:Am J Transplant

Author:

Srinivas T. R.¹,Taber D. J.²,Su Z.³,Zhang J.³,Mour G.¹,Northrup D.⁴,Tripathi A.⁵,Marsden J. E.³,Moran W. P.³,Mauldin P. D.³

Affiliation:

1. Division of Nephrology; Medical University of South Carolina; Charleston SC

2. Division of Transplant Surgery; Medical University of South Carolina; Charleston SC

3. Division of General Internal Medicine & Geriatrics; Medical University of South Carolina; Charleston SC

4. Office of the Chief Information Officer; Medical University of South Carolina; Charleston SC

5. IBM Corporation; Armonk NY

Publisher

Wiley

Subject

Pharmacology (medical),Transplantation,Immunology and Allergy

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1111/ajt.14099/fullpdf

Reference25 articles.

1. Lack of improvement in renal allograft survival despite a marked decrease in acute rejection rates over the most recent era;Meier-Kriesche;Am J Transplant,2004

2. Overview of large database analysis in renal transplantation;Kaplan;Am J Transplant,2003

3. Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation;Taber;Transplantation,2015

4. Sequelae of early hospital readmission after kidney transplantation;McAdams-Demarco;Am J Transplant,2014

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