Plataforma de apoio à decisão nos cuidados de ginecologia e obstetrícia

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Informática Médica)

Massa intracardíaca em provável caso de granulomatose de Wegener

A granulomatose de Wegener, doença conhecida há aproximadamente 65 anos, continua sendo um dilema para a classe médica, talvez pelo seu polimorfismo clínico, talvez pela carência de métodos diagnósticos. Seu diagnóstico laboratorial repousa no binômio: dosagem do anticorpo anti-neutrófilo em sua fração citoplasmática (ANCA-c) e na obtenção de material para análise anatomopatológica. Descrevemos aqui, o caso de um paciente, em cuja evolução clínica pôde ser observada todo aspecto proteiforme desta doença, chamando atenção para o envolvimento cardíaco. Este último considerado por muitos como pouco usual, manifestou-se clinicamente sob a forma de miocardite, pericardite e de uma massa intracardíaca.

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

The Supplementary Material for this article can be found online at: http://journal.frontiersin.org/article/10.3389/fmicb. 2016.00275

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

An object-oriented model for rapid prototyping of data path/control systems - a case study

Publicado em "Information control in manufacturing 1998 : (INCOM'98) : advances in industrial engineering : a proceedings volume from the 9th IFAC Symposium, Nancy-Metz, France, 24-26 June 1998. Vol. 2"

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Estimation of statistical cure from cancer using population-based data

Dissertação de mestrado em Estatística

The alarm distress baby scale in a longitudinal Portuguese study reanalyzed with attachment data

Article first published online: 13 NOV 2013

Prevalência e preditores de sintomatologia depressiva após o parto

Contexto: A depressão pós-parto é uma patologia que ocorre nas primeiras semanas após o parto com conseqüências negativas não só para a mãe, como também para o bebê e para a família. Objetivo: Examinar a prevalência de depressão após o parto, bem como as circunstâncias suscetíveis de predizer a sintomatologia depressiva 1 semana e 3 meses após o parto. Métodos: 197 grávidas preencheram o Questionário de Antecipação do Parto (QAP) (Costa et al., 2005a) no segundo trimestre de gestação. Na primeira semana após o parto, responderam ao Questionário de Experiência e Satisfação com o Parto (QESP) (Costa et al., 2005b) e à Edinburgh Postnatal Depression Scale (EPDS) (Augusto et al., 1996), esta última aplicada novamente no terceiro mês do puerpério. Resultados: Uma percentagem significativa de mulheres encontra-se clinicamente deprimida (EPDS ≥ 13) na primeira semana e 3 meses após o parto (12,4% e 13,7%, respectivamente). Das que têm EPDS ≥ 13 na primeira semana, 25% estão ainda deprimidas 3 meses após o parto. Circunstâncias relativas à saúde física, à experiência emocional de parto e ao primeiro contato com o bebê predizem a sintomatologia depressiva na primeira semana do puerpério. A sintomatologia depressiva na primeira semana após o parto e a experiência emocional negativa de parto predizem a sintomatologia depressiva 3 meses após o parto. Conclusões: Constata-se a importância da experiência emocional de parto e do primeiro contato com o bebê, enfatizando a necessidade de atender às necessidades psicológicas da mulher.

Antecipação e experiência emocional de parto

Este estudo tem como objectivo geral analisar a forma como as mulheres por um lado antecipam e, por outro lado, experienciam emocionalmente o parto do seu primeiro filho. Foi também nosso interesse averiguar a relação entre a antecipação e a experiência real de parto. Para o efeito, 197 grávidas primíparas, com idades compreendidas entre 15 e 39 anos e utentes da Consulta Externa de Obstetrícia da Maternidade Júlio Dinis (Porto) participaram no estudo. Após consentimento informado as participantes preencheram um Questionário Socio-demográfico e o Questionário de Antecipação do Parto (QAP, Costa, Figueiredo, Pacheco, Marques, & Pais, 2005) no 2º trimestre de gravidez. Na primeira semana após o parto foram novamente contactadas as participantes na Unidade de Internamento na Maternidade de Júlio Dinis no sentido de responderem ao Questionário de Experiência e Satisfação com o Parto (QESP, Costa, Figueiredo, Pacheco, Marques, & Pais, 2005). Os resultados mostram que o planeamento do parto parece ser benéfico para algumas mulheres em termos do medo, dor e preocupação em relação ao bebé durante o parto. Deste modo, a implementação de medidas que promovam a informação, suporte emocional e envolvimento nas tomadas de decisão por parte dos serviços de saúde materno-infantis poderiam constituir uma mais-valia para o melhoramento das experiências dos pais.

Pervasive decision support to predict football corners and goals by means of data mining

Football is considered nowadays one of the most popular sports. In the betting world, it has acquired an outstanding position, which moves millions of euros during the period of a single football match. The lack of profitability of football betting users has been stressed as a problem. This lack gave origin to this research proposal, which it is going to analyse the possibility of existing a way to support the users to increase their profits on their bets. Data mining models were induced with the purpose of supporting the gamblers to increase their profits in the medium/long term. Being conscience that the models can fail, the results achieved by four of the seven targets in the models are encouraging and suggest that the system can help to increase the profits. All defined targets have two possible classes to predict, for example, if there are more or less than 7.5 corners in a single game. The data mining models of the targets, more or less than 7.5 corners, 8.5 corners, 1.5 goals and 3.5 goals achieved the pre-defined thresholds. The models were implemented in a prototype, which it is a pervasive decision support system. This system was developed with the purpose to be an interface for any user, both for an expert user as to a user who has no knowledge in football games.

Towards a pervasive data mining engine: architecture overview

Current data mining engines are difficult to use, requiring optimizations by data mining experts in order to provide optimal results. To solve this problem a new concept was devised, by maintaining the functionality of current data mining tools and adding pervasive characteristics such as invisibility and ubiquity which focus on their users, providing better ease of use and usefulness, by providing autonomous and intelligent data mining processes. This article introduces an architecture to implement a data mining engine, composed by four major components: database; Middleware (control); Middleware (processing); and interface. These components are interlinked but provide independent scaling, allowing for a system that adapts to the user’s needs. A prototype has been developed in order to test the architecture. The results are very promising and showed their functionality and the need for further improvements.

Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.