Sistema de deteção de eventos adversos baseado num data warehouse

RESUMO - A Segurança do Doente tem assumido uma relevância crescente nas organizações de saúde, resultado da divulgação de diversos estudos que revelaram a magnitude deste problema e simultaneamente, de uma maior pressão por parte da opinião pública e da comunicação social. Este estudo pretende desenvolver e avaliar a performance de um sistema eletrónico de deteção de eventos adversos, baseado num Data Warehouse, por comparação com os resultados obtidos pela metodologia tradicional de revisão dos registos clínicos. O objetivo principal do trabalho consistiu em identificar um conjunto de triggers / indicadores de alerta que permitam detetar potenciais eventos adversos mais comuns. O sistema desenvolvido apresentou um Valor Preditivo Positivo de 18.2%, uma sensibilidade de 65.1% e uma especificidade de 68.6%, sendo constituído por nove indicadores baseados em informação clínica e 445 códigos do ICD-9-CM, relativos a diagnósticos e procedimentos. Apesar de terem algumas limitações, os sistemas eletrónicos de deteção de eventos adversos apresentam inúmeras potencialidades, nomeadamente a utilização em tempo real e em complemento a metodologias já existentes. Considerando a importância da problemática em análise e a necessidade de aprofundar os resultados obtidos neste trabalho de projeto, seria relevante a sua extensão a um universo mais alargado de instituições hospitalares, estando a sua replicabilidade facilitada, uma vez que o Data Warehouse tem por base um conjunto de aplicações disseminadas a nível nacional. O desenvolvimento e a consolidação dos sistemas eletrónicos de deteção de eventos adversos constitui inegavelmente uma área de futuro, com reflexos ao nível da melhoria da informação existente nas organizações e que contribuirá decisivamente para a melhoria dos cuidados de saúde prestados aos doentes.

On-line system for faults detection in induction motors based on PCA

Dissertation to obtain the degree of Master in Electrical and Computer Engineering

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

The emergence of product certification systems as trade barriers

This paper addresses the growing difficulties automobile manufacturers face within their after sales business: an increasing number of trade obstacles set up by import countries discriminates against the foreign suppliers and impedes the international sales of genuine parts. The purpose of the study is to explore the emergence of trade restrictive product certification systems, which affect spare parts exports of automobile manufacturers. The methodology used includes review of the literature and an empirical study based on qualitative interviews with representatives of major stakeholders of the automotive after sales business. Relevant key drivers, which initiate the introduction of technical regulations in importing countries, are identified and analysed to evaluate their effect on the emerging trade policy. The analysis of the key drivers outlines that several interacting components, such as the global competitiveness of the country, macroeconomic and microeconomic factors, and certain country-specific variables induce trade restrictive product certification systems. The findings allow for an early detection of the emergence of product certification systems and provide a means to early recognise the risks and opportunities for the sales of automotive spare parts in the automakers’ target markets. This allows the manufacturers to react immediately and adapt in time to the upcoming changes.