Neural Networks Ensemble for Cyclosporine Concentration Monitoring

This paper proposes the use of neural networks ensemble for predicting the cyclosporine A (CyA)concen tration in kidney transplant patients. In order to optimize clinical outcomes and to reduce the cost associated with patient care, accurate prediction of CyA concentrations is the main objective of therapeutic drug monitoring. Thirty-two renal allograft patients and different factors (age, weight, gender, creatinine and post-transplantation days, together with past dosages and concentrations)w ere studied to obtain the best models. Three kinds of networks (multilayer perceptron, FIR network, Elman recurrent network) and the formation of neural-network ensembles were used. The FIR network, y…

Use of neural networks for dosage individualisation of erythropoietin in patients with secondary anemia to chronic renal failure.

The external administration of recombinant human erythropoietin is the chosen treatment for those patients with secondary anemia due to chronic renal failure undergoing periodic hemodialysis. The goal is to carry out an individualised prediction of the erythropoietin dosage to be administered. It is justified because of the high cost of this medication, its secondary effects and the phenomenon of potential resistance which some individuals suffer. One hundred and ten patients were included in this study and several factors were collected in order to develop the neural models. Since the results obtained were excellent, an easy-to-use decision-aid computer application was implemented.

A Novel Approach to Introducing Adaptive Filters Based on the LMS Algorithm and Its Variants

This paper presents a new approach to introducing adaptive filters based on the least-mean-square (LMS) algorithm and its variants in an undergraduate course on digital signal processing. Unlike other filters currently taught to undergraduate students, these filters are nonlinear and time variant. This proposal introduces adaptive filtering in the context of a linear time-invariant system using a real problem. In this way, introducing adaptive filters using concepts already familiar to the students motivates their interest through practical application. The key point for this simplification is that the input to the filter is constant so that the adaptive filter becomes linear. Therefore, a …

Some Examples for Solving Clinical Problems Using Neural Networks

In this paper neural networks are presented for solving some pharmaceutical problems. We have predicted and prevented patients with potential risk of post-Chemotherapy Emesis and potentially intoxicated patients treated with Digoxin. Neural networks have been also used for predicting Cyclosporine A concentration and Erythropoietin concentrations. Several neural networks (multilayer perceptron for classification tasks and Elman and FIR networks for prediction) and classical methods have been used. Results show how neural networks are very suitable tools for classification and prediction tasks, outperforming the classical methods. In a neural approach it is not strictly necessary to assume a …

Unificación de enseñanzas relacionadas con el Tratamiento Digital de Señales en la Universitat de València

Teaching Digital Signal Processing (DSP) at the University of Valencia takes place in several subjects with a great overlap in contents. This work proposes an approach to unify DSP contents, so that it is possible to extract the contents corresponding with each subject. Moreover, the proposal is based on a repository that contains the common contents; the repository is hosted on a virtual learning environment, being Moodle and DotLRN the two platforms under study. The final implemented solution consists in an HTMLrepository formed by a number of basic teaching units. These basic units are packaged in a standard IMS-CP format, making up the materials of the different subjects.

Sistemas de ayuda a la decisión clínica (2009/2010)