Hemoglobin Level Analysis in Hemodialysis Patients Treated With Erythropoiesis Stimulating Agents

In this chapter authors try to develop an expert system with the help of neural network method like Organizing Maps (SOMs) for hemodialysis patient.  Neural network models play a very important role for data analysis of hemodialysis patients with end-stage renal disease.  There are two main goals: firstly, the knowledge extraction from a database using Self-Organizing Maps (SOMs); and secondly, to provide an accurate prediction of Hb levels next month.

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…

Qualitative Analysis of Feed Management Practice on Goat Herds by Self Organizing Maps in Murcia Region of Spain

Abstract Fernandez, C., Soria, E., Magdalena, R., Martin, J.D. and Mata, C. 2007. Qualitative analysis of feed management practice on goat herds by self organizing maps in Murcia region of Spain. J. Appl. Anim. Res., 32: 41–47. Self organizing maps (SOM) were used to analyze data from ninety four herds. Data were obtained from surveys and management practices were evaluated. The 18% of farms were dairy goat farms with milking machines, with a herd size of 100 to 200 goats and most of these bought compound feed. 12% has the same characteristics but farmers prepared their own diet. 16% were similar to previous prototypes, but farmers in addition to dairy goat production kept sheep as well. 23…

Self organizing maps as a novel tool for data analysis in education

Young people currently live and are connected to the virtual world in a natural and simple way. Nevertheless, in spite of the great advantages of the use of Information and Communication Technology, and particularly social networks, there are several drawbacks, principally security and privacy of net users. However, human behaviour is strongly non-linear, so usual statistical analysis does not yield accurate results. Now, machine learning algorithms are very common in solving real life non-linear problems, such as economics, medicine and engineering. So it would be worthy to apply this methodology on education data sets. In this work, a non-linear, visual algorithm named Self Organizing Map…

Semi-Supervised Classification Method for Hyperspectral Remote Sensing Images

A new approach to the classification of hyperspectral images is proposed. The main problem with supervised methods is that the learning process heavily depends on the quality of the training data set. In remote sensing, the training set is useful only for simultaneous images or for images with the same classes taken under the same conditions; and, even worse, the training set is frequently not available. On the other hand, unsupervised methods are not sensitive to the number of labelled samples since they work on the whole image. Nevertheless, relationship between clusters and classes is not ensured. In this context, we propose a combined strategy of supervised and unsupervised learning met…

Urban monitoring using multi-temporal SAR and multi-spectral data

In some key operational domains, the joint use of synthetic aperture radar (SAR) and multi-spectral sensors has shown to be a powerful tool for Earth observation. In this paper, we analyze the potentialities of combining interferometric SAR and multi-spectral data for urban area characterization and monitoring. This study is carried out following a standard multi-source processing chain. First, a pre-processing stage is performed taking into account the underlying physics, geometry, and statistical models for the data from each sensor. Second, two different methodologies, one for supervised and another for unsupervised approaches, are followed to obtain features that optimize the urban rela…

Robust adaptive algorithm with low computational cost

An adaptive algorithm, which is robust to impulsive noise, is proposed. The cost function underlying this algorithm contains a parameter that controls the immunity to impulsive noise and can be easily adapted. Moreover, weight updating involves a nonlinear function, which recently has been shown to have an efficient hardware implementation. The proposed adaptive algorithm has been successfully tested in terms of accuracy and convergence on a system-identification simulation.

Neural Models for Rainfall Forecasting

This chapter is focused on obtaining an optimal forecast of one month lagged rainfall in Spain. It is assessed by analyzing 22 years of both satellite observations of vegetation activity (e.g. NDVI) and climatic data (precipitation, temperature). The specific influence of non-spatial climatic indices such as NAO and SOI is also addressed. The approaches considered for rainfall forecasting include classical Auto-Regressive Moving-Average with Exogenous Inputs (ARMAX) models and Artificial Neural Networks (ANN), the so-called Multilayer Perceptron (MLP), in particular. The use of neural models is proven to be an adequate mathematical prediction tool in this problem due the non-linearity of th…

Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks

In this paper, we present the use of different mathematical models to forecast service requests in support centers (SCs). A successful prediction of service request can help in the efficient management of both human and technological resources that are used to solve these eventualities. A nonlinear analysis of the time series indicates the convenience of nonlinear modeling. Neural models based on the time delay neural network (TDNN) are benchmarked with classical models, such as auto-regressive moving average (ARMA) models. Models achieved high values for the correlation coefficient between the desired signal and that predicted by the models (values between 0.88 and 0.97 were obtained in th…

The PHES battery does not detect all cirrhotic patients with early neurological deficits, which are different in different patients.

Background and aims The psychometric hepatic encephalopathy score (PHES) is the “gold standard” for minimal hepatic encephalopathy (MHE) diagnosis. Some reports suggest that some cirrhotic patients “without” MHE according to PHES show neurological deficits and other reports that neurological alterations are not homogeneous in all cirrhotic patients. This work aimed to assess whether: 1) a relevant proportion of cirrhotic patients show neurological deficits not detected by PHES; 2) cirrhotic patients with mild neurological deficits are a homogeneous population or may be classified in sub-groups according to specific deficits. Methods Cirrhotic patients “without” (n = 56) or “with” MHE (n = 4…

Herramienta para la generación de mapas de rendimiento en cítricos usando los datos de una plataforma de asistencia a la recolección con sistema de clasificación

El uso de nuevas tecnologías y la creación de mapas de rendimiento son una herramienta clave para cuantificar la información de un cultivo. La representación espacial de esta información registrada durante la recolección permite una mayor eficiencia en la gestión del cultivo por parte del agricultor, lo que a su vez repercute en una reducción de los costes de producción.Este trabajo presenta una herramienta para la monitorización del rendimiento en cultivos cítricos y la creación de mapas del cultivo en base a la información obtenida durante la recolección. La información es adquirida por una plataforma móvil de asistencia a la recolección de cítricos creada en el IVIA con sistema de inspec…

Clinical Decision Support System To Prevent Toxicity In Patients Treated With Digoxin

In this chapter, authors develop a system for prevention and detection of congestive heart failure and fibrillation. Due to its narrow therapeutic range more than 10% of the patients treated with DGX can suffer toxic effects, but it is estimated that half of the cases of digitalis toxicity could be prevented. Two multivariate models were developed to prevent digitalis toxicity.

Design of a Neural Network Model as a Decision Making Aid in Renal Transplant

This paper presents the application of this new tool of data processing in the study of the problem that arises when a renal transplant is indicated for a paediatric patient. Its aim is the development and validation of a neural network based model which can predict the success of the transplant over the short, medium and long term, using pre-operative characteristics of the patient (recipient) and implant organ (donor). When compared to results of logistic regression, the results of the proposed model showed better performance. Once the model is obtained, it will be converted into a tool for predicting the efficiency of the transplant protocol in order to optimise the donor-recipient pair …

Feature selection using ROC curves on classification problems

Feature Selection (FS) is one of the key stages in classification problems. This paper proposes the use of the area under Receiver Operator Characteristic curves to measure the individual importance of every input as well as a method to discover the variables that yield a statistically significant improvement in the discrimination power of the classification model.

Preface to Data Mining in Biomedical Informatics and Healthcare

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 …

ETAT: Expository Text Analysis Tool.

Qualitative methods that analyze the coherence of expository texts not only are time consuming, but also present challenges in collecting data on coding reliability. We describe software that analyzes expository texts more rapidly and produces a notable level of objectivity. ETAT (Expository Text Analysis Tool) analyzes the coherence of expository texts. ETAT adopts a symbolic representational system, known as conceptual graph structures. ETAT follows three steps: segmentation of a text into nodes, classification of the unidentified nodes, and linking the nodes with relational arcs. ETAT automatically constructs a graph in the form of nodes and their interrelationships, along with various a…

BioLab: An Educational Tool for Signal Processing Training in Biomedical Engineering

This paper introduces and evaluates BioLab, a tool for teaching biosignal processing. BioLab has been used in the biomedical engineering module that is given in the second semester of the fifth year of the electronic engineering degree at the University of Valencia, Spain. This module and its correspondent curricular pathway are also reviewed. BioLab allows the results obtained with digital processing techniques to be shown interactively in the theory classes, and it also provides support in laboratory sessions. The graphic interface of BioLab simplifies its learning and use and provides access to processing and visualization functions by means of menus. The tool is based on Matlab since th…

Qualitative analysis of goat and sheep production data using self-organizing maps

The aim of this study was to analyse the relationship between different small ruminant livestock production systems with different levels of specialization. The analysis is carried out by using the self-organizing map. This tool allows high-dimensional input spaces to be mapped into much lower-dimensional spaces, thus making it much more straightforward to understand any set of data. These representations enable the visual extraction of qualitative relationships among variables (visual data mining), converting the data to maps. The data used in this study were obtained from surveys completed by farmers who are principally dedicated to goat and sheep production. With the self-organizing map …

Classical Training Methods

This chapter reviews classical training methods for multilayer neural networks. These methods are widely used for classification and function modelling tasks. Nevertheless, they show a number of flaws or drawbacks that should be addressed in the development of such systems. They work by searching the minimum of an error function which defines the optimal behaviour of the neural network. Different standard problems are used to show the capabilities of these models; in particular, we have benchmarked the algorithms in a nonlinear classification problem and in three function modelling problems.

Adaptive algorithms robust to impulsive noise with low computational cost using order statistic

Abstract In this paper a family of adaptive algorithms robust to impulsive noise and with low computational cost are presented. Unlike other approaches, no cost functions or filtering of the gradient are considered in order to update the filter coefficients. Its initial basis is the basic LMS algorithm and its sign-error variant. The proposed algorithms can be considered as some sign-error variants of the LMS algorithm. The algorithms are successfully tested in terms of accuracy and convergence in a standard system identification simulation in which an impulsive noise is present. Simulations show that they improve the performance of LMS variants that are robust to impulsive noise.

Neural networks as effective techniques in clinical management of patients: some case studies

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 input output 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: identificatio…

Survival prediction in patients undergoing ischemic cardiopathy

The ischemic cardiopathy is the main cause of death in developed countries. New improved drugs and therapies have appeared last years. However, the interventionist strategy and the most powerful drugs may have complications, and hence, it is very important to know the risk of death associated with patients during their stay in the hospital, or in the next six months. Thus, it is possible to tune the best treatment for each individual patient. In this framework, the use of artificial neural networks is proposed with a double objective: survival prediction and the extraction of the parameters with best predictive capabilities. A cohort of 691 patients treated in the Hospital Clinic, in Barcel…

Corrigendum to “Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks” [Expert Systems with Applications 34/1 (2008) 665–672]

Characterization and Modelization of Surface Net Radiation through Neural Networks

Artificial neural networks have shown to be a powerful tool for system modeling in a wide range of applications. In this chapter, the focus is on neural network applications to obtain qualitative/quantitative relationships between meteorological and soil parameters and net radiation, the latter being a significant term of the surface energy balance equation. By using a Multilayer Perceptron model an artificial neural network based on the above mentioned parameters, net radiation was estimated over a vineyard crop. A comparison has been made between the estimates provided by the Multilayer Perceptron and a linear regression model that only uses solar incoming shortwave radiation as input par…

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 …

Application of Neural Networks in Animal Science

Stock breeding has been one of the most important sources of food and labour throughout human history. Every advance in this field has always led to important and beneficial impacts on human society. These innovations have mainly taken place in machines or genetics, but data analysis has been somewhat ignored. Most of the published works in data analysis use linear models, and there are few works in the literature that use non-linear methods for data processing in stock breeding where these methods have proven to obtain better results and performance than linear, classical methods. This chapter demonstrates the use of non-linear methods by presenting two practical applications: milk yield p…

6 Self-organising Maps To Analyse Effects Of Low Intensity Concentric Combined With Blood Flow Restriction

Introduction Self-Organising Maps (SOM) are a type of Artificial Neural Network (ANN) model used to visualise multidimensional data [Kohonen-00]. SOM provides a correspondence between the original N-dimensional space and a twodimensional space [Haykin-08]. In the case presented in this work, this means that two subjects with similar values of the different variables should appear near in the two-dimensional space [Kohonen-00], [Haykin-08]. Method In order to test this method, 15 athletes were selected. Subjects’ measurements were performed in three different periods: before the exercise test, immediately after performing the exercise and twenty-four hours later. The control measures were th…

Neural networks for animal science applications: Two case studies

Abstract Artificial neural networks have shown to be a powerful tool for system modelling in a wide range of applications. In this paper, we focus on neural network applications to intelligent data analysis in the field of animal science. Two classical applications of neural networks are proposed: time series prediction and clustering. The first task is related to the prediction of weekly milk production in goat flocks, which includes a knowledge discovery stage in order to analyse the relative relevance of the different variables. The second task is the clustering of goat flocks; it is used to analyse different livestock surveys by using self-organizing maps and the adaptive resonance theo…