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.

Forest cover monitoring in the Bara district (Nepal) with remote sensing and geographic information systems

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Land use / land cover change detection: an object oriented approach, Münster, Germany

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies

Static detection of anomalies in transactional memory programs

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia Informática

Advanced image processing techniques for detection and quantification of drusen

Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa, Faculty of Sciences and Technology

New analytical methodologies for doping control – detection of anabolic androgenic steroids in human urine

Dissertation submitted to Faculdade de Ciências e Tecnologia - Universidade Nova de Lisboa in fulfilment of the requirements for the degree of Doctor of Philosophy (Biochemistry - Biotechnology)

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

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

Time series morphological analysis applied to biomedical signals events detection

Dissertation submitted in the fufillment of the requirements for the Degree of Master in Biomedical Engineering

Amorphous silicon e 3D sensors applied to object detection

Nowadays, existing 3D scanning cameras and microscopes in the market use digital or discrete sensors, such as CCDs or CMOS for object detection applications. However, these combined systems are not fast enough for some application scenarios since they require large data processing resources and can be cumbersome. Thereby, there is a clear interest in exploring the possibilities and performances of analogue sensors such as arrays of position sensitive detectors with the final goal of integrating them in 3D scanning cameras or microscopes for object detection purposes. The work performed in this thesis deals with the implementation of prototype systems in order to explore the application of object detection using amorphous silicon position sensors of 32 and 128 lines which were produced in the clean room at CENIMAT-CEMOP. During the first phase of this work, the fabrication and the study of the static and dynamic specifications of the sensors as well as their conditioning in relation to the existing scientific and technological knowledge became a starting point. Subsequently, relevant data acquisition and suitable signal processing electronics were assembled. Various prototypes were developed for the 32 and 128 array PSD sensors. Appropriate optical solutions were integrated to work together with the constructed prototypes, allowing the required experiments to be carried out and allowing the achievement of the results presented in this thesis. All control, data acquisition and 3D rendering platform software was implemented for the existing systems. All these components were combined together to form several integrated systems for the 32 and 128 line PSD 3D sensors. The performance of the 32 PSD array sensor and system was evaluated for machine vision applications such as for example 3D object rendering as well as for microscopy applications such as for example micro object movement detection. Trials were also performed involving the 128 array PSD sensor systems. Sensor channel non-linearities of approximately 4 to 7% were obtained. Overall results obtained show the possibility of using a linear array of 32/128 1D line sensors based on the amorphous silicon technology to render 3D profiles of objects. The system and setup presented allows 3D rendering at high speeds and at high frame rates. The minimum detail or gap that can be detected by the sensor system is approximately 350 μm when using this current setup. It is also possible to render an object in 3D within a scanning angle range of 15º to 85º and identify its real height as a function of the scanning angle and the image displacement distance on the sensor. Simple and not so simple objects, such as a rubber and a plastic fork, can be rendered in 3D properly and accurately also at high resolution, using this sensor and system platform. The nip structure sensor system can detect primary and even derived colors of objects by a proper adjustment of the integration time of the system and by combining white, red, green and blue (RGB) light sources. A mean colorimetric error of 25.7 was obtained. It is also possible to detect the movement of micrometer objects using the 32 PSD sensor system. This kind of setup offers the possibility to detect if a micro object is moving, what are its dimensions and what is its position in two dimensions, even at high speeds. Results show a non-linearity of about 3% and a spatial resolution of < 2µm.

Methodological framework for detection of harmonisation breaking in service environments

As the complexity of markets and the dynamicity of systems evolve, the need for interoperable systems capable of strengthening enterprise communication effectiveness increases. This is particularly significant when it comes to collaborative enterprise networks, like manufacturing supply chains, where several companies work, communicate, and depend on each other, in order to achieve a specific goal. Once interoperability is achieved, that is once all network parties are able to communicate with and understand each other, organisations are able to exchange information along a stable environment that follows agreed laws. However, as markets adapt to new requirements and demands, an evolutionary behaviour is triggered giving space to interoperability problems, thus disrupting the sustainability of interoperability and raising the need to develop monitoring activities capable of detecting and preventing unexpected behaviour. This work seeks to contribute to the development of monitoring techniques for interoperable SOA-based enterprise networks. It focuses on the automatic detection of harmonisation breaking events during real-time communications, and strives to develop and propose a methodological approach to handle these disruptions with minimal or no human intervention, hence providing existing service-based networks with the ability to detect and promptly react to interoperability issues.

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.