Artificial intelligence techniques: predicting necessity for biopsy in renal transplant recipients suspected of acute cellular rejection or nephrotoxicity.

Autor: Hummel AD; Programa de Pós-graduação em Informática em Saúde, Universidade Federal de São Paulo, São Paulo, Brazil., Maciel RF, Sousa FS, Cohrs FM, Falcão AE, Teixeira F, Baptista R, Mancini F, da Costa TM, Alves D, Rodrigues RG, Miranda R, Pisa IT
Jazyk: angličtina
Zdroj: Transplantation proceedings [Transplant Proc] 2011 May; Vol. 43 (4), pp. 1343-4.
DOI: 10.1016/j.transproceed.2011.02.029
Abstrakt: The gold standard for nephrotoxicity and acute cellular rejection (ACR) is a biopsy, an invasive and expensive procedure. More efficient strategies to screen patients for biopsy are important from the clinical and financial points of view. The aim of this study was to evaluate various artificial intelligence techniques to screen for the need for a biopsy among patients suspected of nephrotoxicity or ACR during the first year after renal transplantation. We used classifiers like artificial neural networks (ANN), support vector machines (SVM), and Bayesian inference (BI) to indicate if the clinical course of the event suggestive of the need for a biopsy. Each classifier was evaluated by values of sensitivity and area under the ROC curve (AUC) for each of the classifiers. The technique that showed the best sensitivity value as an indicator for biopsy was SVM with an AUC of 0.79 and an accuracy rate of 79.86%. The results were better than those described in previous works. The accuracy for an indication of biopsy screening was efficient enough to become useful in clinical practice.
(Copyright © 2011 Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
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