Navegando por Palavras-chave "Monitoramento de medicamentos/efeitos adversos"

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    Aplicação de redes neurais artificiais em transplantes renais: classificação de nefrotoxicidade e rejeição celular aguda
    (Universidade Federal de São Paulo (UNIFESP), 2010-10-27) Maciel, Rafael Fabio [UNIFESP]; Pisa, Ivan Torres [UNIFESP]; Universidade Federal de São Paulo (UNIFESP)
    BACKGROUND: Complications associated with kidney transplantation and immunosuppression can be prevented or treated effectively if diagnosed in early stages with monitoring post-transplant. OBJECTIVE: To present the results of comparison of automatic pattern classifiers using different techniques of artificial intelligence to predict events of nephrotoxicity and acute cellular rejection (RCA), with up to one year of renal transplantation METHODS: Statistical tests were performed on the prevalence and linear regression in variables regarding nephrotoxicity and RCA. We used different classifiers (neural networks, support vector machines (SVM), decision trees, Bayesian inference, and closest neighbors) in order to provide RCA and nephrotoxicity. The classifiers were evaluated according to the value of sensitivity, specificity and area under ROC curve (AUC). RESULTS: The prevalence of acute cellular rejection was 31.0% and 26.9% of nephrotoxicity. The technique had the highest sensitivity value prediction for the submission to the transplanted kidney biopsy was SVM (LIBSVM algorithm) with sensitivity rates of 0.87 (accuracy rate 79.86; specificity 0.70; AUC 0.79). The technique had the highest AUC for predicting nephrotoxicity and RCA was bayesian inference (NaiveBayes), with AUC rates of 0.8 (accuracy rate 75.92). CONCLUSION: The results are encouraging, with rates of trial and error consistent with the determination of acute cellular rejection and nephrotoxicity.