title: Artificial intelligence techniques: predicting necessity for biopsy in renal transplant recipients suspected of acute cellular rejection or nephrotoxicity.
abstract: 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.
authors.affiliation: Programa de Pós-graduação em Informática em Saúde, Universidade Federal de São Paulo, São Paulo, Brazil.
authors.forename: A D
authors.initials: AD
authors.lastname: Hummel
authors.forename: R F
authors.initials: RF
authors.lastname: Maciel
authors.forename: F S
authors.initials: FS
authors.lastname: Sousa
authors.forename: F M
authors.initials: FM
authors.lastname: Cohrs
authors.forename: A E J
authors.initials: AE
authors.lastname: Falcão
authors.forename: F
authors.initials: F
authors.lastname: Teixeira
authors.forename: R
authors.initials: R
authors.lastname: Baptista
authors.forename: F
authors.initials: F
authors.lastname: Mancini
authors.forename: T M
authors.initials: TM
authors.lastname: da Costa
authors.forename: D
authors.initials: D
authors.lastname: Alves
authors.forename: R G D S
authors.initials: RG
authors.lastname: Rodrigues
authors.forename: R
authors.initials: R
authors.lastname: Miranda
authors.forename: I T
authors.initials: IT
authors.lastname: Pisa
mesh_terms: D000208:Acute Disease; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001706:Biopsy; D020000:Decision Support Systems, Clinical; D006084:Graft Rejection / Q000175:diagnosis* / Q000209:etiology; D006801:Humans; D007166:Immunosuppressive Agents / Q000009:adverse effects; D007674:Kidney Diseases / Q000175:diagnosis* / Q000209:etiology; D016030:Kidney Transplantation / Q000009:adverse effects*; D016571:Neural Networks, Computer; D018579:Patient Selection; D011237:Predictive Value of Tests; D012372:ROC Curve
chemical_list: D007166:Immunosuppressive Agents