Application of Artificial Neural Networks in Renal Transplantation: Classification of Nephrotoxicity and Acute Cellular Rejection Episodes

Abstract Complications associated with kidney transplantation and immunosuppression can be prevented or treated effectively if diagnosed in the early stages by posttransplant monitoring. One of the major problems is diseases that occur during the first year after kidney transplantation. For this pur...

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Bibliographic Details
Published in:	Transplantation proceedings Vol. 42; no. 2; pp. 471 - 472
Main Authors:	Hummel, A.D, Maciel, R.F, Rodrigues, R.G.S, Pisa, I.T
Format:	Journal Article Conference Proceeding
Language:	English
Published:	Amsterdam Elsevier Inc 01-03-2010 Elsevier
Subjects:	Acute Disease Biological and medical sciences Fundamental and applied biological sciences. Psychology Fundamental immunology Graft Rejection - epidemiology Humans Immunosuppressive Agents - therapeutic use Kidney Transplantation - immunology Kidney Transplantation - pathology Kidney Transplantation - statistics & numerical data Medical sciences Neural Networks (Computer) Patient Selection Postoperative Complications - epidemiology Postoperative Complications - pathology Retrospective Studies ROC Curve Surgery Surgery (general aspects). Transplantations, organ and tissue grafts. Graft diseases Surgery of the urinary system Tacrolimus - therapeutic use Tissue, organ and graft immunology Waiting Lists Kidney disease Graft(material) Urinary system disease Toxicity Acute Neural network Homotransplantation Kidney Medicine Rejection Treatment Urinary system Surgery Classification Graft Application Cell Nephrotoxicity Kidney transplantation
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Summary:	Abstract Complications associated with kidney transplantation and immunosuppression can be prevented or treated effectively if diagnosed in the early stages by posttransplant monitoring. One of the major problems is diseases that occur during the first year after kidney transplantation. For this purpose, we used different classifiers to predict events of nephrotoxicity versus acute cellular rejection episodes. The classifiers were evaluated according to values of sensitivity, specificity and area under ROC curves (RCA). The classifier with better accuracy rate for nephrotoxicity achieved the value of 75.68% and RCA classifier reached the accuracy of 80.89%. These results are encouraging, with rates of accuracy and error consistent with work purpose.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0041-1345 1873-2623
DOI:	10.1016/j.transproceed.2010.01.051