Linked Data based Health Information Representation, Visualization and Retrieval System on the Semantic Web

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

Web-application for gathering, analyzing, and processing health information about allergy data

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

Web-assisted tobacco intervention in Portuguese : intentions to make behavioural changes and behavioural changes

ABSTRACT - The problem of how to support “intentions to make behavioural changes” (IBC) and “behaviour changes” (BC) in smoking cessation when there is a scarcity of resources is a pressing issue in public health terms. The present research focuses on the use of information and communications technologies and their role in smoking cessation. It is developed in Portugal after the ratification of WHO Framework Convention on Tobacco Control (on 8 November 2005). The prevalence of smokers over fifteen years of age within the population stood at 20.9% (30.9% for men and 11.8% for women). While the strategy of helping people to quit smoking has been emphasised at National Health Service (NHS) level, the uptake of cessation assistance has exceeded the capacity of the service. This induced the search of new theoretical and practical venues to offer alternative options to people willing to stop smoking. Among these, the National Health Plan (NHP) of Portugal (2004-2010), identifies the use of information technologies in smoking cessation. eHealth and the importance of health literacy as a means of empowering people to make behavioural changes is recurrently considered an option worth investigating. The overall objective of this research is to understand, in the Portuguese context, the use of the Internet to help people to stop smoking. Research questions consider factors that may contribute to “intentions to make behavioural changes” (IBC) and “behavioural changes” (BC) while using a Web-Assisted Tobacco Intervention Probe (WATIP). Also consideration is given to the trade-off on the use of the Web as a tool for smoking cessation: can it reach a vast number of people for a small cost (efficiency) demonstrating to work in the domain of smoking cessation (efficacy)”? In addition to the introduction, there is a second chapter in which the use of tobacco is discussed as a public health menace. The health gains achieved by stopping smoking and the means of quitting are also examined, as is the use of the Internet in smoking cessation. Then, several research issues are introduced. These include background theory and the theoretical framework for the Sense of Coherence. The research model is also discussed. A presentation of the methods, materials and of the Web-Assisted Tobacco Intervention Probe (WATIP) follows. In chapter four the results of the use of the Web-Assisted Tobacco Intervention Probe (WATIP) are presented. This study is divided into two sections. The first describes results related to quality control in relation to the Web-Assisted Tobacco Intervention Probe (WATIP) and gives an overview of its users. Of these, 3,150 answered initial eligibility questions. In the end, 1,463 met all eligibility requirements, completed intake, decided on a day to quit smoking (Dday) and declared their “intentions to make behavioural changes” (IBC) while a second targeted group of 650 did not decide on a Dday. With two quit attempts made before joining the platform, most of the participants had experienced past failures while wanting to stop. The smoking rate averaged 21 cigarettes per day. With a mean age of 35, of the participants 55% were males. Among several other considerations, gender and the Sense of Coherence (SOC) influenced the success of participants in their IBC and endeavour to set quit dates. The results of comparing males and females showed that, for current smokers, establishing a Dday was related to gender differences, not favouring males (OR=0.76, p<0.005). Belonging to higher Socio-economic strata (SES) was associated with the intention to consider IBC (when compared to lower SES condition) (OR=1.57, p<0.001) and higher number of school years (OR=0.70, p<0.005) favoured the decision to smoking cessation. Those who demonstrated higher confidence in their likelihood of success in stopping in the shortest time had a higher rate of setting a Dday (OR=0.51, p<0.001). There were differences between groups in IBC reflecting the high and low levels of the SOC score (OR=1.43, p=0.006), as those who considered setting a Dday had higher levels of SOC. After adjusting for all variables, stages of readiness to change and SOC were kept in the model. This is the first Arm of this research where the focus is a discussion of the system’s implications for the participants’ “intentions to make behavioural changes” (IBC). Moreover, a second section of this study (second Arm) offers input collected from 77 in-depth interviews with the Web-Assisted Tobacco Intervention Probe (WATIP) users. Here, “Behaviour Change” (BC) and the usability of the platform are explored a year after IBC was declared. A percentage of 32.9% of self-reported, 12-month quitters in continuous abstinence from smoking from Dday to the 12-month follow- up point of the use of the Web-Assisted Tobacco Intervention Probe (WATIP) has been assessed. Comparing the Sense of Coherence (SOC) scores of participants by their respective means, according to the two groups, there was a significant difference in these scores of non smokers (BC) (M=144,66, SD=22,52) and Sense of Coherence (SOC) of smokers (noBC) (M=131,51, SD=21,43) p=0.014. This WATIP strategy and its contents benefit from the strengthening of the smoker’s sense of coherence (SOC), so that the person’s progress towards a life without tobacco may be experienced as comprehensible, manageable and meaningful. In this sample the sense of coherence (SOC) effect is moderate although it is associated with the day to quit smoking (Dday). Some of the limitations of this research have to do with self-selection bias, sample size (power) and self-reporting (no biochemical validation). The enrolment of participants was therefore not representative of the smoking population. It is not possible to verify the Web-Assisted Tobacco Intervention Probe (WATIP) evaluation of external validity; consequently, the results obtained cannot be applied generalized. No participation bias is provided. Another limitation of this study is the associated limitations of interviews. Interviewees’ perception that fabricating answers could benefit them more than telling the simple truth in response to questions is a risk that is not evaluated (with no external validation like measuring participants’ carbon monoxide levels). What emerges in this analysis is the relevance of the process that leads to the establishment of the quit day (Dday) to stop using tobacco. In addition, technological issues, when tailoring is the focus, are key elements for scrutiny. The high number of dropouts of users of the web platform mandates future research that should concentrate on the matters of the user-centred design of portals. The focus on gains in health through patient-centred care needs more research, so that technology usability be considered within the context of best practices in smoking cessation.

On the cloud deployment of a session abstraction for service/data aggregation

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Web Processing Services for Forestry and Environmental Applications

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

New visualization model for large scale biosignals analysis

Benefits of long-term monitoring have drawn considerable attention in healthcare. Since the acquired data provides an important source of information to clinicians and researchers, the choice for long-term monitoring studies has become frequent. However, long-term monitoring can result in massive datasets, which makes the analysis of the acquired biosignals a challenge. In this case, visualization, which is a key point in signal analysis, presents several limitations and the annotations handling in which some machine learning algorithms depend on, turn out to be a complex task. In order to overcome these problems a novel web-based application for biosignals visualization and annotation in a fast and user friendly way was developed. This was possible through the study and implementation of a visualization model. The main process of this model, the visualization process, comprised the constitution of the domain problem, the abstraction design, the development of a multilevel visualization and the study and choice of the visualization techniques that better communicate the information carried by the data. In a second process, the visual encoding variables were the study target. Finally, the improved interaction exploration techniques were implemented where the annotation handling stands out. Three case studies are presented and discussed and a usability study supports the reliability of the implemented work.

Complex networks and data mining: toward a new perspective for the understanding of complex systems

Complex systems, i.e. systems composed of a large set of elements interacting in a non-linear way, are constantly found all around us. In the last decades, different approaches have been proposed toward their understanding, one of the most interesting being the Complex Network perspective. This legacy of the 18th century mathematical concepts proposed by Leonhard Euler is still current, and more and more relevant in real-world problems. In recent years, it has been demonstrated that network-based representations can yield relevant knowledge about complex systems. In spite of that, several problems have been detected, mainly related to the degree of subjectivity involved in the creation and evaluation of such network structures. In this Thesis, we propose addressing these problems by means of different data mining techniques, thus obtaining a novel hybrid approximation intermingling complex networks and data mining. Results indicate that such techniques can be effectively used to i) enable the creation of novel network representations, ii) reduce the dimensionality of analyzed systems by pre-selecting the most important elements, iii) describe complex networks, and iv) assist in the analysis of different network topologies. The soundness of such approach is validated through different validation cases drawn from actual biomedical problems, e.g. the diagnosis of cancer from tissue analysis, or the study of the dynamics of the brain under different neurological disorders.

Integrated data model and DSL modifications

Companies are increasingly more and more dependent on distributed web-based software systems to support their businesses. This increases the need to maintain and extend software systems with up-to-date new features. Thus, the development process to introduce new features usually needs to be swift and agile, and the supporting software evolution process needs to be safe, fast, and efficient. However, this is usually a difficult and challenging task for a developer due to the lack of support offered by programming environments, frameworks, and database management systems. Changes needed at the code level, database model, and the actual data contained in the database must be planned and developed together and executed in a synchronized way. Even under a careful development discipline, the impact of changing an application data model is hard to predict. The lifetime of an application comprises changes and updates designed and tested using data, which is usually far from the real, production, data. So, coding DDL and DML SQL scripts to update database schema and data, is the usual (and hard) approach taken by developers. Such manual approach is error prone and disconnected from the real data in production, because developers may not know the exact impact of their changes. This work aims to improve the maintenance process in the context of Agile Platform by Outsystems. Our goal is to design and implement new data-model evolution features that ensure a safe support for change and a sound migration process. Our solution includes impact analysis mechanisms targeting the data model and the data itself. This provides, to developers, a safe, simple, and guided evolution process.

Analysis of panoramio photo tags in order to extract land use information

In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Saúde em Portugal : estudo das urgências hospitalares através do Data Mining

Qualquer assunto relacionado com a saúde é sempre um tema sensível, pela importância que tem junto da população, já que interage diretamente com o bem-estar das pessoas e, essencialmente, com a sensação de segurança que as estas pretendem ter na prestação dos cuidados básicos de saúde. Dados estatísticos mostram que a população está cada vez mais envelhecida, reforçando a importância da existência de bons centros hospitalares e de um bom Sistema Nacional de Saúde (SNS) (Plano Nacional de Saúde, 2010). Em Portugal, caso os pacientes necessitem de cuidados mais urgentes, podem recorrer ao Serviço de Urgências disponibilizado para toda a população através do SNS. No entanto, a gestão e planeamento deste serviço é complexa, dado este serviço ser frequentemente utilizado por pacientes que não necessitam de cuidados urgentes, levando a que os hospitais deixem de conseguir dar a resposta esperada, implicando a prestação por vezes um serviço de menor qualidade. Neste sentido, analisaram-se dados de um hospital do norte do país com o intuito de perceber o ponto de situação das urgências, de forma a encontrar padrões relevantes através da análise de clusters e de regras de associação. Começando pela análise de clusters, utilizaram-se apenas as variáveis que foram consideradas importantes para o problema, resultando da análise final 3 clusters. O primeiro cluster é constituído por elementos do sexo masculino de todas as idades, o segundo cluster por elementos do sexo masculino mais jovens e por elementos do sexo feminino até aos 60 anos e o terceiro cluster apenas por elementos do sexo feminino a partir dos 40 anos. No final verificaram-se muitas semelhanças entre os clusters 1 e 3, pois ambos continham os pacientes mais idosos, havendo um padrão comum no seu comportamento. No ano 2012 não houve registo de nenhuma epidemia, não havendo por isso nenhuma doença que se destacasse comparativamente às restantes. Concluiu-se também que na maior parte dos casos houve a necessidade de uma intervenção urgente (pulseira de cor Amarela), no entanto a maioria dos pacientes observados conseguiu regressar às suas habitações após as consultas nas Urgências Hospitalares, sem intervenções médicas adicionais. Relativamente às regras de associação, houve a necessidade de transformar e eliminar algumas variáveis que enviesassem o estudo. Após o processo da criação das regras de associação, percebeu-se que as regras eram muito similares entre si, apresentando uma maior confiança nas variáveis que apareceram em maior número (“Pacientes com pulseira de cor Amarela”, “distrito do Porto” ou “Alta Médica para a Residência”).

Combining data mining and evolutionary computation for multi-criteria optimization of earthworks

Earthworks tasks aim at levelling the ground surface at a target construction area and precede any kind of structural construction (e.g., road and railway construction). It is comprised of sequential tasks, such as excavation, transportation, spreading and compaction, and it is strongly based on heavy mechanical equipment and repetitive processes. Under this context, it is essential to optimize the usage of all available resources under two key criteria: the costs and duration of earthwork projects. In this paper, we present an integrated system that uses two artificial intelligence based techniques: data mining and evolutionary multi-objective optimization. The former is used to build data-driven models capable of providing realistic estimates of resource productivity, while the latter is used to optimize resource allocation considering the two main earthwork objectives (duration and cost). Experiments held using real-world data, from a construction site, have shown that the proposed system is competitive when compared with current manual earthwork design.

A proactive intelligent decision support system for predicting the popularity of online news

"Lecture notes in computer science series, ISSN 0302-9743, vol. 9273"

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Rockburst laboratory tests database - Application of data mining techniques

Rockburst is characterized by a violent explosion of a block causing a sudden rupture in the rock and is quite common in deep tunnels. It is critical to understand the phenomenon of rockburst, focusing on the patterns of occurrence so these events can be avoided and/or managed saving costs and possibly lives. The failure mechanism of rockburst needs to be better understood. Laboratory experiments are undergoing at the Laboratory for Geomechanics and Deep Underground Engineering (SKLGDUE) of Beijing and the system is described. A large number of rockburst tests were performed and their information collected, stored in a database and analyzed. Data Mining (DM) techniques were applied to the database in order to develop predictive models for the rockburst maximum stress (σRB) and rockburst risk index (IRB) that need the results of such tests to be determined. With the developed models it is possible to predict these parameters with high accuracy levels using data from the rock mass and specific project.