Depressão em pacientes HIV positivos: a realidade de um hospital português

Aims: Depression is the most common psychiatric disorder among people infected with HIV. This study aims to characterize the Hospital of Joaquim Urbano population of HIV-infected patients’ profile regarding depressive symptoms and whether they correlate with the analytical parameters most frequently evaluated in the context of infection by this virus – HIV viral load, CD4+ count and CD4+ percentage. Methods: We conducted an observational descriptive and analytical study. The participants’ level of depressive symptoms was assessed with the Beck Depression Inventory. The medical and psychiatric history and the analytical values of viral load, CD4+ count and CD4+ percentage were obtained by consulting the participants’ clinical processes. Results: A prevalence of 65.5% in HIV-infected patients’ depressive symptoms was found, with a considerable high percentage of subjects presenting with severe symptoms (32.7%). No associations between the depressive symptoms’ levels and CD4+ count, CD4+ percentage or viral load were found. However, depressive symptoms were associated with substance abuse and education level. Conclusions: The high prevalence of depressive symptoms found in this study reinforces the importance of monitoring this type of symptoms in HIV-infected subjects. The fact that there have been no associations between depressive symptoms and the analytical parameters evaluated is in line with previous studies.

Ferramenta de suporte à decisão e prática clínica em unidades de cuidados neonatais e pediátricos

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

O papel da comunicação estratégica nas Instituições de Saúde: o caso do Hospital de Braga

Relatório de estágio de mestrado em Ciências da Comunicação (área de especialização em Publicidade e Relações Públicas)

Aplicação de princípios e ferramentas lean numa unidade de cirurgia de ambulatório de um hospital

Dissertação de mestrado integrado em Engenharia Industrial

Pervasive patient timeline for intensive care units

This research work explores a new way of presenting and representing information about patients in critical care, which is the use of a timeline to display information. This is accomplished with the development of an interactive Pervasive Patient Timeline able to give to the intensivists an access in real-time to an environment containing patients clinical information from the moment in which the patients are admitted in the Intensive Care Unit (ICU) until their discharge This solution allows the intensivists to analyse data regarding vital signs, medication, exams, data mining predictions, among others. Due to the pervasive features, intensivists can have access to the timeline anywhere and anytime, allowing them to make decisions when they need to be made. This platform is patient-centred and is prepared to support the decision process allowing the intensivists to provide better care to patients due the inclusion of clinical forecasts.

Optimization techniques to detect early ventilation extubation in intensive care units

The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hydrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2%, 93.1%, 92.97% respectively, thus showing their feasibility to work in a real environment.