Step towards multiplatform framework for supporting pediatric and neonatology care unit decision process

Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Sucesso escolar e percursos vocacionais na adolescência: variáveis pessoais e contextuais

Tese de Doutoramento Ciências da Educação (Especialidade em Psicologia da Educação)

Treating the entire person

[Excerpt] A critical case from a Portuguese hospital reveals how the ultimate healthcare customer, the patient, is a complete system, not a jumble of parts. (...) The lean production philosophy has made inroads into service sectors, including medical care in the United Kingdom and the United States. Unfortunately, numerous medical organizations in those two countries and the rest of the world treat patients like they are made up of parts, not as a whole system. This leads to disjointed handoffs, bottlenecks in information flow that delay treatment, and sending the patient back and forth from department to department. The following case in Portugal shows how most of the world’s health systems still suffer from functional silos and how waste is all over the place. In this case, the missing links in communication between doctors, nurses, auxiliary staff, the patient and her family led to the patient’s death. Adopting lean healthcare with its proven tools would be a solution to many of the problems described. When a patient dies in a hospital, the family often is told that the doctors did everything they could. Normally, that is the case, as healthcare providers – doctors, nurses, auxiliary staff, therapists – do their best with the system they have.

"Virar a Sala de Aula”: centrar a aprendizagem no aluno recorrendo a ferramentas cognitivas

Relatório de projeto de mestrado em Ensino de Informática

Aprender a debater: o debate como atividade de interação e de participação democrática

Relatório de estágio de mestrado em Ensino de Inglês e de Espanhol no 3º Ciclo do Ensino Básico e no Ensino Secundário

A avaliação da comunicação da criança com perturbação do espetro do autismo: perspetiva de pais e de profissionais

Dissertação de mestrado em Educação Especial (área de especialização Intervenção Precoce)

HEP-Frame: A software engineered framework to aid the development and efficient multicore execution of scientific code

This work was supported by FCT (Fundação para a Ciência e Tecnologia) within Project Scope (UID/CEC/00319/2013), by LIP (Laboratório de Instrumentação e Física Experimental de Partículas) and by Project Search-ON2 (NORTE-07-0162- FEDER-000086), co-funded by the North Portugal Regional Operational Programme (ON.2 - O Novo Norte), under the National Strategic Reference Framework, through the European Regional Development Fund.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Perceções de profissionais acerca dos benefícios da utilização da entrevista baseada nas rotinas no apoio às famílias em intervenção precoce

Dissertação de mestrado em Educação Especial (área de especialização em Intervenção Precoce)

Práticas centradas na família: um estudo de caso do Centro de Educación Infantil y de Atención Temprana da Universidade Católica de Valência

Dissertação de mestrado em Educação Especial (área de especialização em Intervenção Precoce)