Método de estratificación de territorios basado en sistemas de información geográfica y medidas de similitud geométrica / Territory Stratification Method based on Geographic Information Systems and Geometric Similarity Measures

Una meta del sistema de salud es la prevención de enfermedades, por ello cobra especial importancia el estudio de la relación de enfermedades con el espacio. Existen evidencias del empleo de los Sistemas de Información Geográfica en estudios sobre la distribución espacial de problemas de salud. A pesar de esto, los trabajos reportados en la literatura consultada no explotan la componente espacial de los datos, lo que limita su integralidad. Por otra parte, existe dispersión en las metodologías, herramientas y técnicas para abordar estudios de este tipo. En esta investigación se presenta un método de estratificación de territorios basado en Sistemas de Información Geográfica y medidas de similitud geométrica, definidas a partir de los criterios: distancia, tamaño y conectividad. La propuesta permite realizar estudios estratificados según la primera ley de la geografía y garantiza la obtención de estratos más compactos. El método propuesto cuenta con cinco etapas: Selección de indicadores y territorios, Preprocesamiento de indicadores, Agrupamiento, Postprocesamiento y Visualización, soportado en una solución informática basada en software libre. Como parte de la validación se aplica el método en un caso de estudio y se realiza el análisis de índices de validación que avalan la efectividad y competitividad de la propuesta(AU)

A goal of the health system is the prevention of diseases, which is why the study of the relationship of diseases with space is of special importance. There is evidence of the use of Geographic Information Systems in studies on the spatial distribution of health problems. Despite this, the works reported in the consulted literature do not exploit the spatial component of the data, which limits its comprehensiveness. On the other hand, there is dispersion in the methodologies, tools and techniques to approach studies of this type. This research presents a method of stratification of territories based on Geographic Information Systems and geometric similarity measures, defined from the criteria: distance, size and connectivity. The proposal allows for stratified studies according to the first law of geography and guarantees the obtaining of more compact strata. The proposed method has five stages: Selection of indicators and territories, Pre-processing of indicators, Grouping, Post-processing and Visualization, supported by a computer solution based on free software. As part of the validation, the method is applied in a case study and the analysis of validation indices is carried out that guarantee the effectiveness and competitiveness of the proposal(AU)

Mining drug-disease relationships:a recommendation system / 中国药理学通报

Aim Drug repositioning is to find new indications for existing drugs,however,potential drug-disease relationships are often hidden in millions of unknown relationship.With the analyzing of medical big data,we predict the potential drug-dis-ease relationships.Methods Based on the assumption that similar drugs tend to have similar indications,we applied a rec-ommendation-based strategy to drug repositioning.First,we col-lected the information of known drug-disease therapeutic effect, side effect,drug characters and disease characters;second,we calculated the drug-drug similarity measurements and disease-disease similarity measurements;last,we used a collaborative filtering (CF)method to predict unknown drug-disease relation-ships based on the known drug-disease relationships by integra-ting the similarity measurements,and built a ranking list of pre-diction results.Results The experiments demonstrated that a-mong the TOP 500 of the list,1 2.8% were supported by clinical experiments or review,and 20% were supported by model or-ganism or cell experiments.Conclusion Compared to the clas-sification model and random sampling results,the CF model can effectively reduce the false positives.

Similaridades entre semiologias na metodologia da aprendizagem baseada em problemas / Similarities between medical semiologies in the problem-based learning methodology

O Programa de Aprendizagem de Ginecologia e Obstetrícia da PUC-PR adota a aprendizagem baseada em problemas como metodologia de aprendizagem. Neste programa, são executados diversos casos clínicos que compõem seu conteúdo. Para cada caso clínico, os professores definem uma semiologia principal e as respectivas semiologias secundárias, a fim de definir o conjunto de ações ou procedimentos que os alunos devem executar. Visando à inserção das tecnologias da informação e comunicação no aprendizado dos alunos, foi desenvolvido um sistema que permite obter, passo a passo, as ações e procedimentos executados pelos alunos. O objetivo geral deste trabalho é conceber um modelo matemático e computacional que permita obter índices de similaridade entre as semiologias definidas pelos professores e as executadas pelos alunos.

The Undergraduate Course in Obstetrics and Gynecology at the Catholic University in Paraná State, Brazil, is based on problem-based learning. The program analyzes various clinical cases to form the course content. For each clinical case, the professors define the primary patient work-up protocol and the corresponding clinical/diagnostic techniques, in order to establish the set of measures or procedures that students are expected to perform. In order to include information and communication technologies in the students' learning process, developed a system that allows the students to follow a step-by-step approach to these measures and procedures. The overall objective of the current article was to design a mathematical and computational model that allows obtaining indices of similarity between the patient work-up approaches defined by the professors and those actually performed by students.