Modelo para el análisis de la mortalidad en Colombia 2000-2012 / A model for mortality analysis in Colombia. 2000-2012

RESUMEN Objetivo Proponer y evaluar un modelo para el ajuste y predicción de la mortalidad en Colombia que permita analizar tendencias por edad, sexo, Departamento y causa. Metodología Los registros de defunciones no fetales fueron utilizados como fuente primaria de análisis. Estos datos se pre-procesaron recodificando las causas y redistribuyendo los códigos basura. El modelo de predicción se formuló como una aproximación lineal de un conjunto de variables de interés, en particular la población y el producto interno bruto departamental. Resultados Como caso particular de estudio se tomó la mortalidad de menores de 5 años, se observó una disminución sostenida a partir del año 2000 tanto a nivel nacional como departamental, con excepción de tres departamentos. La evaluación del poder predictivo de la metodología propuesta se realizó ajustando el modelo con los datos de 2000 a 2011, la predicción para el 2012 fue comparada con la tasa observada, estos resultados muestran que el modelo es suficientemente confiable para la mayor parte de las combinaciones departamento-causa. Conclusiones La metodología y modelo propuesto tienen el potencial de convertirse en un instrumento que permita orientar las prioridades del gasto en salud utilizando algún tipo de evidencia.(AU)

ABSTRACT Objective To propose and evaluate a model for fitting and forecasting the mortality rates in Colombia that allows analyzing the trends by age, sex, region and cause of death. Methodology The national death registries were used as primary source of analysis. The data was pre-processed recodifying the cause of death and redistributing the garbage codes. The forecast model was formulated as a linear approximation with a set of variables of interest, in particular the population and gross domestic product (GDP) by region. Results As study case we took the mortality under 5 years old, it decreased steadily since 2000 at the national level and at most of the regions. The predictive power of the proposed methodology was tested by fitting the model with the data from 2000 to 2011, the forecast for 2012 was compared with the actual rate, and these results show the model is reliable enough for most of the region-cause combinations. Conclusions The proposed methodology and model have the potential to become an instrument to guide health spending priorities using some kind of evidence.(AU)

Characterizing Architectural Distortion in Mammograms by Linear Saliency.

Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the complex superposition of breast tissue. This paper presents a novel AD characterization by representing the linear saliency in mammography Regions of Interest (ROI) as a graph composed of nodes corresponding to locations along the ROI boundary and edges with a weight proportional to the line intensity integrals along the path connecting any pair of nodes. A set of eigenvectors from the adjacency matrix is then used to extract discriminant coefficients that represent those nodes with higher salient lines. A dimensionality reduction is further accomplished by selecting the pair of nodes with major contribution for each of the computed eigenvectors. The set of main salient lines is then assembled as a feature vector that inputs a conventional Support Vector Machine (SVM). Experimental results with two benchmark databases, the mini-MIAS and DDSM databases, demonstrate that the proposed linear saliency domain method (LSD) performs well in terms of accuracy. The approach was evaluated with a set of 246 RoI extracted from the DDSM (123 normal tissues and 123 AD) and a set of 38 ROI from the mini-MIAS collections (19 normal tissues and 19 AD) respectively. The classification results showed respectively for both databases an accuracy rate of 89 % and 87 %, a sensitivity rate of 85 % and 95 %, and a specificity rate of 93 % and 84 %. Likewise, the area under curve (A z ) of the Receiver Operating Characteristic (ROC) curve was 0.93 for both databases.

Automatic colposcopy video tissue classification using higher order entropy-based image registration.

Colposcopy is a well-established method to detect and diagnose intraepithelial lesions and uterine cervical cancer in early stages. During the exam color and texture changes are induced by the application of a contrast agent (e.g.3-5% acetic acid solution or iodine). Our aim is to densely quantify the change in the acetowhite decay level for a sequence of images captured during a colposcopy exam to help the physician in his diagnosis providing new tools that overcome subjectivity and improve reproducibility. As the change in acetowhite decay level must be calculated from the same tissue point in all images, we present an elastic image registration scheme able to compensate patient, camera and tissue movement robustly in cervical images. The image registration is based on a novel multi-feature entropy similarity criterion. Temporal features are then extracted using the color properties of the aligned image sequence and a dual compartment tissue model of the cervix. An example of the use of the temporal features for pixel-wise classification is presented and the results are compared against ground truth histopathological annotations.