Politicization of non-political events

Spatial analysis and social network analysis typically take into consideration social processes in specific contexts of geographical or network space. The research in political science increasingly strives to model heterogeneity and spatial dependence. To better understand and geographically model the relationship between “non-political” events, streaming data from social networks, and political climate was the primary objective of the current study. Geographic information systems (GIS) are useful tools in the organization and analysis of streaming data from social networks. In this study, geographical and statistical analysis were combined in order to define the temporal and spatial nature of the data eminating from the popular social network Twitter during the 2014 FIFA World Cup. The study spans the entire globe because Twitter’s geotagging function, the fundamental data that makes this study possible, is not limited to a geographic area. By examining the public reactions to an inherenlty non-political event, this study serves to illuminate broader questions about social behavior and spatial dependence. From a practical perspective, the analyses demonstrate how the discussion of political topics fluсtuate according to football matches. Tableau and Rapidminer, in addition to a set basic statistical methods, were applied to find patterns in the social behavior in space and time in different geographic regions. It was found some insight into the relationship between an ostensibly non-political event – the World Cup - and public opinion transmitted by social media. The methodology could serve as a prototype for future studies and guide policy makers in governmental and non-governmental organizations in gauging the public opinion in certain geographic locations.

Questionário de satisfação do utente e do profissional : impacto num laboratório de análises clínicas

RESUMO: Temos assistido a uma evolução impressionante nos laboratórios de análises clínicas, os quais precisam de prestar um serviço de excelência a custos cada vez mais competitivos. Nos laboratórios os sistemas de gestão da qualidade têm uma importância significativa nesta evolução, fundamentalmente pela procura da melhoria continua, que ocorre não só ao nível de processos e técnicas, mas também na qualificação dos diferentes intervenientes. Um dos problemas fundamentais da gestão e um laboratório é a eliminação de desperdícios e erros criando benefícios, conceito base na filosofia LeanThinking isto é “pensamento magro”, pelo que é essencial conseguir monitorizar funções críticas sistematicamente. Esta monitorização, num laboratório cada vez mais focalizado no utente, pode ser efetuada através de sistemas e tecnologias de informação, sendo possível contabilizar número de utentes, horas de maior afluência, tempo médio de permanência na sala de espera, tempo médio para entrega de análises, resultados entregues fora da data prevista, entre outros dados de apoio à decisão. Devem igualmente ser analisadas as reclamações, bem como a satisfação dos utentes quer através do feedback que é transmitido aos funcionários, quer através de questionários de satisfação. Usou-se principalmente dois modelos: um proposto pelo Índice Europeu de Satisfação do Consumidor (ECSI) e o outro de Estrutura Comum de Avaliação (CAF). Introduziram-se igualmente dois questionários: um apresentado em formato digital num posto de colheitas, através de um quiosque eletrónico, e um outro na página da internet do laboratório, ambos como alternativa ao questionário em papel já existente, tendo-se analisado os dados, e retirado as devidas conclusões. Propôs-se e desenvolveu-se um questionário para colaboradores cuja intenção foi a de fornecer dados úteis de apoio à decisão, face à importância dos funcionários na interação com os clientes e na garantia da qualidade ao longo de todo o processo. Avaliaram-se globalmente os resultados sem que tenha sido possível apresentá-los por política interna da empresa, bem como se comentou de forma empírica alguns benefícios deste questionário. Os principais objetivos deste trabalho foram, implementar questionários de satisfação eletrónicos e analisar os resultados obtidos, comparando-os com o estudo ECSI, de forma a acentuar a importância da análise em simultâneo de dois fatores: a motivação profissional e a satisfação do cliente, com o intuito de melhorar os sistemas de apoio à decisão. ------------------------ ABSTRACT: We have witnessed an impressive development in clinical analysis laboratories, which have to provide excellent service at increasingly competitive costs, quality management systems have a significant importance in this evolution, mainly by demanding continuous improvement, which does not occur only in terms of processes and techniques, but also in the qualification of the various stakeholders. One key problem of managing a laboratory is the elimination of waste and errors, creating benefits, concept based on Lean Thinking philosophy, therefore it is essential be able to monitor critical tasks systematically. This monitoring, in an increasingly focused on the user laboratory can be accomplished through information systems and technologies, through which it is possible to account the number of clients, peak times, average length of waiting room stay, average time for delivery analysis, delivered results out of the expected date, among other data that contribute to support decisions, however it is also decisive to analyzed complaint sand satisfaction of users through employees feedback but mainly through satisfaction questionnaires that provides accurate results. We use mainly two models one proposed by the European Index of Consumer Satisfaction (ECSI), directed to the client, and the Common Assessment Framework (CAF), used both in the client as the employees surveys. Introduced two questionnaires in a digital format, one in the central laboratory collect center, through an electronic kiosk and another on the laboratory web page, both as an alternative to survey paper currently used, we analyzed the results, and withdrew the conclusions. It was proposed and developed a questionnaire for employees whose intention would be to provide useful data to decision support, given the importance of employees in customer interaction and quality assurance throughout the whole clinical process, it was evaluated in a general way because it was not possible to show the results, however commented an empirical way some benefits of this questionnaire. The main goals of this study were to implement electronic questionnaires and analyze the results, comparing them with the ECSI, in order to emphasize the importance of analyzing simultaneously professional motivation with customer satisfaction, in order to improve decision support systems.

Business intelligence in banking: A literature analysis from 2002 to 2013 using Text Mining and latent Dirichlet allocation

telligence applications for the banking industry. Searches were performed in relevant journals resulting in 219 articles published between 2002 and 2013. To analyze such a large number of manuscripts, text mining techniques were used in pursuit for relevant terms on both business intelligence and banking domains. Moreover, the latent Dirichlet allocation modeling was used in or- der to group articles in several relevant topics. The analysis was conducted using a dictionary of terms belonging to both banking and business intelli- gence domains. Such procedure allowed for the identification of relationships between terms and topics grouping articles, enabling to emerge hypotheses regarding research directions. To confirm such hypotheses, relevant articles were collected and scrutinized, allowing to validate the text mining proce- dure. The results show that credit in banking is clearly the main application trend, particularly predicting risk and thus supporting credit approval or de- nial. There is also a relevant interest in bankruptcy and fraud prediction. Customer retention seems to be associated, although weakly, with targeting, justifying bank offers to reduce churn. In addition, a large number of ar- ticles focused more on business intelligence techniques and its applications, using the banking industry just for evaluation, thus, not clearly acclaiming for benefits in the banking business. By identifying these current research topics, this study also highlights opportunities for future research.

Clustering-based approach for categorizing pregnant women in obstetrics and maternity care

When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients’ route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.

Benchmarking sobre técnicas de otimização para modelos de apoio à decisão na medicina intensiva

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Sistema de apoio à decisão clínica: quality of life system

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Pervasive patient timeline

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Apoio à decisão na análise inteligente de reclamações

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Plataforma de apoio à decisão nos cuidados de ginecologia e obstetrícia

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

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.

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.

A clinical recommendation system to maternity care

Nowadays in healthcare, the Clinical Decision Support Systems are used in order to help health professionals to take an evidence-based decision. An example is the Clinical Recommendation Systems. In this sense, it was developed and implemented in Centro Hospitalar do Porto a pre-triage system in order to group the patients on two levels (urgent or outpatient). However, although this system is calibrated and specific to the urgency of obstetrics and gynaecology, it does not meet all clinical requirements by the general department of the Portuguese HealthCare (Direção Geral de Saúde). The main requirement is the need of having priority triage system characterized by five levels. Thus some studies have been conducted with the aim of presenting a methodology able to evolve the pre-triage system on a Clinical Recommendation System with five levels. After some tests (using data mining and simulation techniques), it has been validated the possibility of transformation the pre-triage system in a Clinical Recommendation System in the obstetric context. This paper presents an overview of the Clinical Recommendation System for obstetric triage, the model developed and the main results achieved.

Improving appropriateness of antibiotic therapy: randomized trial of an intervention to foster reassessment of prescription after 3 days.

OBJECTIVES: Reassessment of ongoing antibiotic therapy is an important step towards appropriate use of antibiotics. This study was conducted to evaluate the impact of a short questionnaire designed to encourage reassessment of intravenous antibiotic therapy after 3 days. PATIENTS AND METHODS: Patients hospitalized on the surgical and medical wards of a university hospital and treated with an intravenous antibiotic for 3-4 days were randomly allocated to either an intervention or control group. The intervention consisted of mailing to the physician in charge of the patient a three-item questionnaire referring to possible adaptation of the antibiotic therapy. The primary outcome was the time elapsed from randomization until a first modification of the initial intravenous antibiotic therapy. It was compared within both groups using Cox proportional-hazard modelling. RESULTS: One hundred and twenty-six eligible patients were randomized in the intervention group and 125 in the control group. Time to modification of intravenous antibiotic therapy was 14% shorter in the intervention group (adjusted hazard ratio for modification 1.28, 95% CI 0.99-1.67, P = 0.06). It was significantly shorter in the intervention group compared with a similar group of 151 patients observed during a 2 month period preceding the study (adjusted hazard ratio 1.17, 95% CI 1.03-1.32, P = 0.02). CONCLUSION: The results suggest that a short questionnaire, easily adaptable to automatization, has the potential to foster reassessment of antibiotic therapy.

Sistema intel•ligent d’ajuda a la decisió pel control remot de tecnologia de membranes per la depuració d’aigües

El principal objectiu del projecte consisteix en desenvolupar l’anàlisi, disseny,desenvolupament i implementació d’un sistema d’ajuda a la decisió (SAD) basat en elconeixement pel control remot i la supervisió de l’operació integrada d’estacionsdepuradores BRM (bioreactor de membranes) pe ra la depuració d’aigües residuals ambexigències de qualitat de reutilització de l’aigua tractada

A Negotiation Style Recommender Based on Computational Ecology in Open Negotiation Environments

The system described herein represents the first example of a recommender system in digital ecosystems where agents negotiate services on behalf of small companies. The small companies compete not only with price or quality, but with a wider service-by-service composition by subcontracting with other companies. The final result of these offerings depends on negotiations at the scale of millions of small companies. This scale requires new platforms for supporting digital business ecosystems, as well as related services like open-id, trust management, monitors and recommenders. This is done in the Open Negotiation Environment (ONE), which is an open-source platform that allows agents, on behalf of small companies, to negotiate and use the ecosystem services, and enables the development of new agent technologies. The methods and tools of cyber engineering are necessary to build up Open Negotiation Environments that are stable, a basic condition for predictable business and reliable business environments. Aiming to build stable digital business ecosystems by means of improved collective intelligence, we introduce a model of negotiation style dynamics from the point of view of computational ecology. This model inspires an ecosystem monitor as well as a novel negotiation style recommender. The ecosystem monitor provides hints to the negotiation style recommender to achieve greater stability of an open negotiation environment in a digital business ecosystem. The greater stability provides the small companies with higher predictability, and therefore better business results. The negotiation style recommender is implemented with a simulated annealing algorithm at a constant temperature, and its impact is shown by applying it to a real case of an open negotiation environment populated by Italian companies