Critical, issues and needs in management of primary immunodeficiency diseases in Latin America

Experts from six Latin American countries met to discuss critical issues and needs in the diagnosis and management of primary immunodeficiency diseases (PIDD). The diagnosis of PIDD is generally made following referral to an immunology centre located in a major city, but many paediatricians and general practitioners are not sufficiently trained to suspect PIDD in the first place. Access to laboratory testing is generally limited, and only some screening tests are typically covered by government health programmes. Specialised diagnostic tests are generally not reimbursed. Access to treatment varies by country reflecting differences in healthcare systems and reimbursement policies. An online PIDD Registry Programme for Latin America has been available since 2009, which will provide information about PIDD epidemiology in the region. Additional collaboration across countries appears feasible in at least two areas: a laboratory network to facilitate the diagnosis of PIDD, and educational programmes to improve PIDD awareness. In total, these collaborations should make it possible to advance the diagnosis and management of PIDD in Latin America. (C) 2010 SEICAP. Published by Elsevier Espana, S.L. All rights reserved.

Efficiency issues of evolutionary k-means

One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper shows that evolutionary techniques conceived to guide the application of k-means can be more computationally efficient than systematic (i.e., repetitive) approaches that try to get around the above-mentioned drawbacks by repeatedly running the algorithm from different configurations for the number of clusters and initial positions of prototypes. To do so, a modified version of a (k-means based) fast evolutionary algorithm for clustering is employed. Theoretical complexity analyses for the systematic and evolutionary algorithms under interest are provided. Computational experiments and statistical analyses of the results are presented for artificial and text mining data sets. (C) 2010 Elsevier B.V. All rights reserved.

On the influence of imputation in classification: practical issues

The substitution of missing values, also called imputation, is an important data preparation task for many domains. Ideally, the substitution of missing values should not insert biases into the dataset. This aspect has been usually assessed by some measures of the prediction capability of imputation methods. Such measures assume the simulation of missing entries for some attributes whose values are actually known. These artificially missing values are imputed and then compared with the original values. Although this evaluation is useful, it does not allow the influence of imputed values in the ultimate modelling task (e.g. in classification) to be inferred. We argue that imputation cannot be properly evaluated apart from the modelling task. Thus, alternative approaches are needed. This article elaborates on the influence of imputed values in classification. In particular, a practical procedure for estimating the inserted bias is described. As an additional contribution, we have used such a procedure to empirically illustrate the performance of three imputation methods (majority, naive Bayes and Bayesian networks) in three datasets. Three classifiers (decision tree, naive Bayes and nearest neighbours) have been used as modelling tools in our experiments. The achieved results illustrate a variety of situations that can take place in the data preparation practice.