Critical behavior in a cross-situational lexicon learning scenario

The associationist account for early word learning is based on the co-occurrence between referents and words. Here we introduce a noisy cross-situational learning scenario in which the referent of the uttered word is eliminated from the context with probability gamma, thus modeling the noise produced by out-of-context words. We examine the performance of a simple associative learning algorithm and find a critical value of the noise parameter gamma(c) above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order tau(-1/2) about gamma(c), where tau is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point. Copyright (C) EPLA, 2012

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Applying validated quality indicators to surgical antibiotic prophylaxis in a Brazilian hospital: Learning what should be learned

Background: Compliance with the best surgical antibiotic prophylaxis practice is usually low despite many published guidelines. Objective: This study investigated compliance with the Hospital Infection Control Committee guideline for antibiotic prophylaxis in a Brazilian hospital using quality indicators. Methods: A retrospective study was carried out from November 2009 to March 2010. Medical records from adult inpatients undergoing cardiac, neurologic, and orthopedic clean surgeries were included. The full compliance index was considered 100% when the antibiotic prophylaxis showed adequacy in all evaluated attributes. Analyses were conducted with 5% significance. Results: Medical records from 101 cardiac, 128 neurologic, and 519 orthopedic surgical patients were evaluated. The compliance index was 4.9%, and the compliance index according to specialty was 5.8%, 3.1%, and 3.0%, respectively, for orthopedic, neurologic, and cardiac surgeries. The attribute route of administration produced the best outcomes, whereas the attribute duration of antibiotic prophylaxis produced the worst. No association was identified between compliance to the attributes and patient characteristics. Conclusion: This study showed a low level of adherence to Hospital Infection Control Committee guidelines for antibiotic prophylaxis. This suggests that different strategies should be implemented to promote the best possible practice in the field of antibiotic prophylaxis with greater surgeon engagement. Copyright (C) 2012 by the Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.