Evaluar el cambio organizacional hacia la cronicidad: una empresa compleja e inteligente / Assessing organizational change towards chronicity: a complex and intelligent labour

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A new feature extraction method for signal classification applied to cord dorsum potential detection.

In the spinal cord of the anesthetized cat, spontaneous cord dorsum potentials (CDPs) appear synchronously along the lumbo-sacral segments. These CDPs have different shapes and magnitudes. Previous work has indicated that some CDPs appear to be specially associated with the activation of spinal pathways that lead to primary afferent depolarization and presynaptic inhibition. Visual detection and classification of these CDPs provides relevant information on the functional organization of the neural networks involved in the control of sensory information and allows the characterization of the changes produced by acute nerve and spinal lesions. We now present a novel feature extraction approach for signal classification, applied to CDP detection. The method is based on an intuitive procedure. We first remove by convolution the noise from the CDPs recorded in each given spinal segment. Then, we assign a coefficient for each main local maximum of the signal using its amplitude and distance to the most important maximum of the signal. These coefficients will be the input for the subsequent classification algorithm. In particular, we employ gradient boosting classification trees. This combination of approaches allows a faster and more accurate discrimination of CDPs than is obtained by other methods.

Estimation of distribution algorithms as logistic regression regularizers of microarray classifiers.

OBJECTIVES: The "large k (genes), small N (samples)" phenomenon complicates the problem of microarray classification with logistic regression. The indeterminacy of the maximum likelihood solutions, multicollinearity of predictor variables and data over-fitting cause unstable parameter estimates. Moreover, computational problems arise due to the large number of predictor (genes) variables. Regularized logistic regression excels as a solution. However, the difficulties found here involve an objective function hard to be optimized from a mathematical viewpoint and a careful required tuning of the regularization parameters. METHODS: Those difficulties are tackled by introducing a new way of regularizing the logistic regression. Estimation of distribution algorithms (EDAs), a kind of evolutionary algorithms, emerge as natural regularizers. Obtaining the regularized estimates of the logistic classifier amounts to maximizing the likelihood function via our EDA, without having to be penalized. Likelihood penalties add a number of difficulties to the resulting optimization problems, which vanish in our case. Simulation of new estimates during the evolutionary process of EDAs is performed in such a way that guarantees their shrinkage while maintaining their probabilistic dependence relationships learnt. The EDA process is embedded in an adapted recursive feature elimination procedure, thereby providing the genes that are best markers for the classification. RESULTS: The consistency with the literature and excellent classification performance achieved with our algorithm are illustrated on four microarray data sets: Breast , Colon , Leukemia and Prostate . Details on the last two data sets are available as supplementary material. CONCLUSIONS: We have introduced a novel EDA-based logistic regression regularizer. It implicitly shrinks the coefficients during EDA evolution process while optimizing the usual likelihood function. The approach is combined with a gene subset selection procedure and automatically tunes the required parameters. Empirical results on microarray data sets provide sparse models with confirmed genes and performing better in classification than other competing regularized methods.

Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks.

Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.

Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS.

The transjugular intrahepatic portosystemic shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff's experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first 6 months after TIPS placement. Actually, there is no risk indicator to identify this subgroup of patients before treatment. An investigation for predicting the survival of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. Four supervised machine learning classifiers are applied to discriminate between both subgroups of patients. The application of several feature subset selection (FSS) techniques has significantly improved the predictive accuracy of these classifiers and considerably reduced the amount of attributes in the classification models. Among FSS techniques, FSS-TREE, a new randomized algorithm inspired on the new EDA (estimation of distribution algorithm) paradigm has obtained the best average accuracy results for each classifier.

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.

Combining the predictions of a set of classifiers has shown to be an effective way to create composite classifiers that are more accurate than any of the component classifiers. There are many methods for combining the predictions given by component classifiers. We introduce a new method that combine a number of component classifiers using a Bayesian network as a classifier system given the component classifiers predictions. Component classifiers are standard machine learning classification algorithms, and the Bayesian network structure is learned using a genetic algorithm that searches for the structure that maximises the classification accuracy given the predictions of the component classifiers. Experimental results have been obtained on a datafile of cases containing information about ICU patients at Canary Islands University Hospital. The accuracy obtained using the presented new approach statistically improve those obtained using standard machine learning methods.

Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches.

In this work we introduce a methodology based on genetic algorithms for the automatic induction of Bayesian networks from a file containing cases and variables related to the problem. The structure is learned by applying three different methods: The Cooper and Herskovits metric for a general Bayesian network, the Markov blanket approach and the relaxed Markov blanket method. The methodologies are applied to the problem of predicting survival of people after 1, 3 and 5 years of being diagnosed as having malignant skin melanoma. The accuracy of the obtained models, measured in terms of the percentage of well-classified subjects, is compared to that obtained by the so-called Naive-Bayes. In the four approaches, the estimation of the model accuracy is obtained from the 10-fold cross-validation method.

Structure of the high-order Boltzmann machine from independence maps.

In this paper we consider the determination of the structure of the high-order Boltzmann machine (HOBM), a stochastic recurrent network for approximating probability distributions. We obtain the structure of the HOBM, the hypergraph of connections, from conditional independences of the probability distribution to model. We assume that an expert provides these conditional independences and from them we build independence maps, Markov and Bayesian networks, which represent conditional independences through undirected graphs and directed acyclic graphs respectively. From these independence maps we construct the HOBM hypergraph. The central aim of this paper is to obtain a minimal hypergraph. Given that different orderings of the variables provide in general different Bayesian networks, we define their intersection hypergraph. We prove that the intersection hypergraph of all the Bayesian networks (N!) of the distribution is contained by the hypergraph of the Markov network, it is more simple, and we give a procedure to determine a subset of the Bayesian networks that verifies this property. We also prove that the Markov network graph establishes a minimum connectivity for the hypergraphs from Bayesian networks.

[Prognostic factors in HIV-infected heroin addicts: a multivariate analysis of nonspecific serological factors in the evolution of the infection]. / Factores pronósticos en heroinómanos infectados por el VIH: análisis multivariable de factores serológicos inespecíficos en la evolución de la infección.

We present a prognostic analysis of the quantifying of serum levels of beta 2 microglobulin, neopterina, IL-2 soluble receptor and three major classes of immunoglobulins, in a group of 68 heroin-addicts infected by the human immune deficiency virus, type I, clinically assessed for a period of at least three years. High levels of any of these unspecific serologic factors were correlated with the illness progression. Survival curves were generated with the categorized variables, showed a significant decrease on the time interval prior to the diagnosis of AIDS, in the patients with these variables assigned on the higher groups, being neopterine and IgA the more predictive factors when the Cox proportional regression model is applied. We conclude that the quantifying of these unspecific serum factors provides a useful information regarding the clinical evolution of heroin-addicts with HIV infection.

Prognostic score in acute meningococcemia.

A prognostic score for children with acute meningococcemia is proposed. We reviewed 176 consecutive patients with acute meningococcemia with ten fatalities admitted to our pediatric ICU in the last 3 yr. The score was obtained from patients in shock, using a stepwise linear discriminant analysis of 18 clinical and laboratory variables on admission. Nine variables showed a significant discriminant power in predicting survival and death: coma, base excess, platelets, glucose, temperature, WBC, sex, purpura, and CSF. The score predicted survival in 100% and death in 91%. The predictive values were significantly better than evaluation by the frequencies of the usual clinical and laboratory variables.