Variability in the interpretation of thorax radiography in a primary care medical community and its reference radiologists

OBJECTIVE: To know the variability in the interpretation (normal/pathological) of posteroanterior view chest radiograph and the associated factors between a medical primary care community and a Gold Standard (GS) from 3 reference radiologists. DESIGN: Crossover study. SETTING: Primary care. Teaching health centers of Elche. METHODS: Interobserver percentage agreement (IPA) and Kappa statistic (K) were used to analyse the interobserver agreement (IA) between the GS and every of the participants in interpretation of 100 radiographs. Intraobserver variability was assessed with a second review. Using uni and multivariant analysis some possibly associated factors were studied. RESULTS: Overall IPA and K were 71.3% (CI 95%: 69.2-73.3) and 0.41 (CI 95%: 0.36-0.46) respectively. Time employed in radiographs reading and time passed after medicine studies finished were independently and significantly associated to higher and lower interobserver agreement results respectively, whereas recent professional experience, permanent job and age were not associated. Professional training is the variable that most explain the inter and intraobserver variability. CONCLUSIONS: Overall IA is acceptable. The IA showed by the participants trained under the MIR system is good but the IA achieved by the rest of participants is not acceptable. It is necessary a training which increase the clinical competence, as it does MIR training.     

Incremental wrapper-based gene selection from microarray data for cancer classification

Gene expression microarray is a rapidly maturing technology that provides the opportunity to assay the expression levels of thousands or tens of thousands of genes in a single experiment. We present a new heuristic to select relevant gene subsets in order to further use them for the classification task. Our method is based on the statistical significance of adding a gene from a ranked-list to the final subset. The efficiency and effectiveness of our technique is demonstrated through extensive comparisons with other representative heuristics. Our approach shows an excellent performance, not only at identifying relevant genes, but also with respect to the computational cost.     

Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection

Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature and here the study centres on the use of the Generalized Radial Basis Function Neural Networks (GRBFNNs). An interesting property of the GRBF is that it can continuously and smoothly reproduce different RBFs by changing a real parameter τ. In addition, the mixed use of different RBF shapes in only one RBFNN is allowed. Generalized Radial Basis Function (GRBF) is based on Generalized Gaussian Distribution (GGD), which adds a shape parameter, τ, to standard Gaussian Distribution. Moreover, this paper describes a hybrid approach, Hybrid Algorithm (HA), which combines evolutionary and gradient-based learning methods to estimate the architecture, weights and node topology of GRBFNN classifiers. The feasibility and benefits of the approach are demonstrated by means of six gene microarray classification problems taken from bioinformatic and biomedical domains. Three filters were applied: Fast Correlation-Based Filter (FCBF), Best Incremental Ranked Subset (BIRS), and Best Agglomerative Ranked Subset (BARS); this was done in order to identify salient expression genes from among the thousands of genes in microarray data that can directly contribute to determining the class membership of each pattern. After different gene subsets were obtained, the proposed methodology was performed using the selected gene subsets as new input variables. The results confirm that the GRBFNN classifier leads to a promising improvement in accuracy.     

Toward emotional interactive videogames for children with autism spectrum disorder

Universal Access in the Information Society - Technology and videogames have been proven as motivating tools for working attention and complex communication skills, especially in children with...     

Evolutionary learning of hierarchical decision rules

This paper describes an approach based on evolutionary algorithms, hierarchical decision rules (HIDER), for learning rules in continuous and discrete domains. The algorithm produces a hierarchical set of rules, that is, the rules are sequentially obtained and must therefore be tried until one is found whose conditions are satisfied. Thus, the number of rules may be reduced because the rules could be inside of one another. The evolutionary algorithm uses both real and binary coding for the individuals of the population. We tested our system on real data from the UCI repository, and the results of a ten-fold cross-validation are compared to C4.5s, C4.5Rules, See5s, and See5Rules. The experiments show that HIDER works well in practice.     

Natural Encoding for Evolutionary Supervised Learning

Some of the most influential factors in the quality of the solutions found by an evolutionary algorithm (EA) are a correct coding of the search space and an appropriate evaluation function of the potential solutions. EAs are often used to learn decision rules from datasets, which are encoded as individuals in the genetic population. In this paper, the coding of the search space for the obtaining of those decision rules is approached, i.e., the representation of the individuals of the genetic population and also the design of specific genetic operators. Our approach, called "natural coding," uses one gene per feature in the dataset (continuous or discrete). The examples from the datasets are also encoded into the search space, where the genetic population evolves, and therefore the evaluation process is improved substantially. Genetic operators for the natural coding are formally defined as algebraic expressions. Experiments with several datasets from the University of California at Irvine (UCI) machine learning repository show that as the genetic operators are better guided through the search space, the number of rules decreases considerably while maintaining the accuracy, similar to that of hybrid coding, which joins the well-known binary and real representations to encode discrete and continuous attributes, respectively. The computational cost associated with the natural coding is also reduced with regard to the hybrid representation. Our algorithm, HlDER*, has been statistically tested against C4.5 and C4.5 Rules, and performed well. The knowledge models obtained are simpler, with very few decision rules, and therefore easier to understand, which is an advantage in many domains. The experiments with high-dimensional datasets showed the same good behavior, maintaining the quality of the knowledge model with respect to prediction accuracy.     

How to Develop More Creative Strategic Plans: Results from an Empirical Study

What factors help and hinder creativity within the strategic planning process? An exploratory study examined the strategic plans of groups of graduates for their own business school. The students were assessed for rigidity of cognitive orientation, and divided into open‐minded groups, and two other groups of more rigid orientation. Strategic plans were rated for originality and relevance. An open minded orientation affected the production of a strategic plan in a positive way. The productivity (quantity) of the more closed‐mind type of group was enhanced in the elaboration of a strategic plan through interventions introducing creativity. The quality of production was only marginally enhanced. We conclude that creativity‐enhancing techniques had a marginal direct effect on efforts to deal with ill‐defined problems such as a strategic plan. Additional indirect effects (by influencing rigid cognitive orientation) may be occurring.     

Effects of Postharvest Putrescine Treatment on Extending Shelf Life and Reducing Mechanical Damage in Apricot

ABSTRACT: Apricots ( Prunus armeniaca L. cv Mauricio) harvested at commercial ripening stage were treated with putrescine (1 mM), then mechanically damaged with a 25 N force and stored at 10 °C for 6 d. Putrescine treatment increased fruit firmness and reduced the bruising zones caused by the mechanical damage. Putrescine-treated fruits (both damaged and nondamaged) showed different physiological behavior than controls. Color change, weight loss, ethylene emission, and respiration rate were reduced in putrescine-treated fruits. The most remarkable effect of the mechanical damage was the significant increase in spermidine concentrations found after the compression in control apricots, which could be considered as a physiological marker of mechanical damage.