Diseño y análisis comparativo de un inventario de indicadores de mortalidad evitable adaptado a las condiciones sanitarias de Colombia

Objetivos. Elaborar un inventario de indicadores de mortalidad evitable (INIME) que permita analizar las fallas en el control de los riesgos de mortalidad predominantes en Colombia y comparar los resultados de su aplicación con los obtenidos mediante dos enfoques ampliamente utilizados. Métodos. Se revisaron los registros oficiales de mortalidad de Colombia de 1985 a 2001; las causas básicas de muerte se clasificaron según la CIE-9. Se seleccionaron los indicadores de mortalidad evitable (ME) mediante un algoritmo que combinó las listas de Holland y de Taucher, la definición de Rutstein y colaboradores y el principio de Uemura. Se compararon las proporciones de muertes evitables resultantes de aplicar el INIME y las dos listas de ME a una base de datos con los registros oficiales de defunciones de Colombia de 1993 a 1996. Resultados. De las 680 617 defunciones registradas en el período de estudio, se clasificaron como evitables 18,2% según la lista de Holland y 51,3% según la lista de Taucher. La ME según el INIME ascendió a 76,7%. Este patrón se mantuvo relativamente estable entre 1993 y 1996. Las diferencias observadas en la proporción de muertes evitables según el INIME y las dos listas de ME se relacionaron con el perfil epidemiológico local y el enfoque conceptual de cada lista. Conclusiones. Las diferencias entre el INIME y las listas de ME de Holland y de Taucher muestran las consecuencias de usar una u otra clasificación en el contexto colombiano. El INIME puede constituir un recurso valioso para fundamentar y evaluar políticas sanitarias, pero debe ajustarse a la situación específica en que se aplique.

Application of Text Summarization techniques to the Geographical Information Retrieval task

Automatic Text Summarization has been shown to be useful for Natural Language Processing tasks such as Question Answering or Text Classification and other related fields of computer science such as Information Retrieval. Since Geographical Information Retrieval can be considered as an extension of the Information Retrieval field, the generation of summaries could be integrated into these systems by acting as an intermediate stage, with the purpose of reducing the document length. In this manner, the access time for information searching will be improved, while at the same time relevant documents will be also retrieved. Therefore, in this paper we propose the generation of two types of summaries (generic and geographical) applying several compression rates in order to evaluate their effectiveness in the Geographical Information Retrieval task. The evaluation has been carried out using GeoCLEF as evaluation framework and following an Information Retrieval perspective without considering the geo-reranking phase commonly used in these systems. Although single-document summarization has not performed well in general, the slight improvements obtained for some types of the proposed summaries, particularly for those based on geographical information, made us believe that the integration of Text Summarization with Geographical Information Retrieval may be beneficial, and consequently, the experimental set-up developed in this research work serves as a basis for further investigations in this field.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

Evaluation of an integrated system for classification, assessment and comparison of services for long-term care in Europe: the eDESDE-LTC study

Background: The harmonization of European health systems brings with it a need for tools to allow the standardized collection of information about medical care. A common coding system and standards for the description of services are needed to allow local data to be incorporated into evidence-informed policy, and to permit equity and mobility to be assessed. The aim of this project has been to design such a classification and a related tool for the coding of services for Long Term Care (DESDE-LTC), based on the European Service Mapping Schedule (ESMS). Methods: The development of DESDE-LTC followed an iterative process using nominal groups in 6 European countries. 54 researchers and stakeholders in health and social services contributed to this process. In order to classify services, we use the minimal organization unit or “Basic Stable Input of Care” (BSIC), coded by its principal function or “Main Type of Care” (MTC). The evaluation of the tool included an analysis of feasibility, consistency, ontology, inter-rater reliability, Boolean Factor Analysis, and a preliminary impact analysis (screening, scoping and appraisal). Results: DESDE-LTC includes an alpha-numerical coding system, a glossary and an assessment instrument for mapping and counting LTC. It shows high feasibility, consistency, inter-rater reliability and face, content and construct validity. DESDE-LTC is ontologically consistent. It is regarded by experts as useful and relevant for evidence-informed decision making. Conclusion: DESDE-LTC contributes to establishing a common terminology, taxonomy and coding of LTC services in a European context, and a standard procedure for data collection and international comparison.

Way of scoring by Spanish First Division volleyball teams in relation to winning/losing, home/away, final classification, and type of confrontation

The aim of this study was to assess the way volleyball teams score with regard to: whether or not they won the game, whether they were the home or away team, the level of the opposing teams, and the type of confrontation. The sample was composed of 118,083 plays from 794 men’s volleyball matches and 125,751 plays from 719 women’s matches of Spain’s first division clubs (from the 2002-2003 season to the 2006-2007 season). The variables studied were: the way points were obtained in each play, being the home or away team, the level of the teams, the result of the match, and the type of confrontation between the teams with regard to their level. The results demonstrate that for both men’s and women’s teams, the majority of the points were obtained in attack and by opponent errors. Differences were found with regard to the way points were obtained when winning or losing the match was taken into account as well as when considering the level of the teams. This paper discusses the differences found with regard to whether the team is home or visiting and the type of confrontation.

An updated evolutionary classification of CRISPR–Cas systems

The evolution of CRISPR–cas loci, which encode adaptive immune systems in archaea and bacteria, involves rapid changes, in particular numerous rearrangements of the locus architecture and horizontal transfer of complete loci or individual modules. These dynamics complicate straightforward phylogenetic classification, but here we present an approach combining the analysis of signature protein families and features of the architecture of cas loci that unambiguously partitions most CRISPR–cas loci into distinct classes, types and subtypes. The new classification retains the overall structure of the previous version but is expanded to now encompass two classes, five types and 16 subtypes. The relative stability of the classification suggests that the most prevalent variants of CRISPR–Cas systems are already known. However, the existence of rare, currently unclassifiable variants implies that additional types and subtypes remain to be characterized.