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In this paper, we present four examples of effective implementation of neural systems in the daily clinical practice. There are two main goals in this work; the first one is to show that neural networks are especially well-suited tools for solving different kind of medical/pharmaceutical problems, given the complex inputÀoutput relationships and the few a priori knowledge about data distribution and variable relations. The second goal is to develop specific software applications, which enclose complex mathematical models, to clinicians; thus, the use of such models as decision support systems is facilitated. Four important pharmaceutical problems are considered in this study: identification of patients with potential risk of postchemotherapy emesis, classification of patients depending on their risk of digoxin intoxication, prediction of cyclosporine A through concentration and prediction of erythropoietin blood concentrations. The Multilayer Perceptron in classification problems and dynamic neural networks, such as the Elman recurrent neural network and the Finite Impulse Response neural network in prediction problems, have been used. Moreover, network ensembles of different kind of networks have been taken into account. Results show that neural networks are suitable tools for medical classification and prediction tasks, outperforming the mostly used methods in these problems (logistic regression and multivariate analysis).

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