Quantifying data quality for clinical trials using electronic data capture.

BACKGROUND: Historically, only partial assessments of data quality have been performed in clinical trials, for which the most common method of measuring database error rates has been to compare the case report form (CRF) to database entries and count discrepancies. Importantly, errors arising from medical record abstraction and transcription are rarely evaluated as part of such quality assessments. Electronic Data Capture (EDC) technology has had a further impact, as paper CRFs typically leveraged for quality measurement are not used in EDC processes. METHODS AND PRINCIPAL FINDINGS: The National Institute on Drug Abuse Treatment Clinical Trials Network has developed, implemented, and evaluated methodology for holistically assessing data quality on EDC trials. We characterize the average source-to-database error rate (14.3 errors per 10,000 fields) for the first year of use of the new evaluation method. This error rate was significantly lower than the average of published error rates for source-to-database audits, and was similar to CRF-to-database error rates reported in the published literature. We attribute this largely to an absence of medical record abstraction on the trials we examined, and to an outpatient setting characterized by less acute patient conditions. CONCLUSIONS: Historically, medical record abstraction is the most significant source of error by an order of magnitude, and should be measured and managed during the course of clinical trials. Source-to-database error rates are highly dependent on the amount of structured data collection in the clinical setting and on the complexity of the medical record, dependencies that should be considered when developing data quality benchmarks.

Deriving the Railway Mania

This article argues that the promotion boom which occurred in the railway industry during the mid 1840s was amplified by the issue of derivative-like assets, which let investors take highly leveraged positions in the shares of new railway companies. The partially paid shares which the new railway companies issued allowed investors to obtain exposure to an asset by paying only a small initial deposit. The consequence of this arrangement was that investor returns were substantially amplified, and many schemes could be financed simultaneously. However, when investors were required to make further payments it put a negative downward pressure on prices.

Common-sense knowledge for a computer vision system for human action recognition

This work presents a novel approach for human action recognition based on the combination of computer vision techniques and common-sense knowledge and reasoning capabilities. The emphasis of this work is on how common sense has to be leveraged to a vision-based human action recognition so that nonsensical errors can be amended at the understanding stage. The proposed framework is to be deployed in a realistic environment in which humans behave rationally, that is, motivated by an aim or a reason. © 2012 Springer-Verlag.

Common-sense reasoning for human action recognition

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm - known as bag of words - gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline. © 2012 Elsevier B.V. All rights reserved.

Predicting the past: Understanding the causes of bank distress in the Netherlands in the 1920s

Why do some banks fail in financial crises while others survive? This article answers this question by analysing the effect of the Dutch financial crisis of the 1920s on 142 banks, of which 33 failed. We find that choices of balance sheet composition and product market strategy made in the lead-up to the crisis had a significant impact on banks’ subsequent chances of experiencing distress. We document that high-risk banks – those operating highly-leveraged portfolios and attracting large quantities of deposits – were more likely to fail. Branching and international activities also increased banks’ default probabilities. We measure the effects of board interlocks, which have been characterized in the extant literature as contributing to the Dutch crisis. We find that boards mattered: failing banks had smaller boards, shared directors with smaller and very profitable banks and had a lower concentration of interlocking directorates in non-financial firms.

Transnational Culture Wars

The well-known ‘culture wars’ clash in the United States between civil society actors has now gone transnational. Political science scholarship has long detailed how liberal human rights non-governmental organizations NGOs engage in extensive transnational activity in support of their ideals. More recently, US conservative groups (including faith-based NGOs) have begun to emulate these strategies, promoting their convictions by engaging in transnational advocacy. NGOs thus face off against each other politically across the globe. Less well known is the extent to which these culture wars are conducted in courts, using conflicting interpretations of human rights law. Many of the same protagonists, particularly NGOs that find themselves against each other in US courts, now find new litigation opportunities abroad in which to fight their battles. These developments, and their implications, are the focus of this article. In particular, the extent to which US faith-based NGOs have leveraged the experience gained transnationally to use international and foreign jurisprudence in interventions before the US Supreme Court is assessed.

Video Lecture Capture : Student Engagement

Video Capture of university lectures enables learners to be more flexible in their learning behaviour, for instance choosing to attend lectures in person or watch later. However attendance at lectures has been linked to academic success and is of concern for faculty staff contemplating the introduction of Video Lecture Capture. This research study was devised to assess the impact on learning of recording lectures in computer programming courses. The study also considered behavioural trends and attitudes of the students watching recorded lectures, such as when, where, frequency, duration and viewing devices used. The findings suggest there is no detrimental effect on attendance at lectures with video materials being used to support continual and reinforced learning with most access occurring at assessment periods. The analysis of the viewing behaviours provides a rich and accessible data source that could be potentially leveraged to improve lecture quality and enhance lecturer and learning performance.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

Knowledge representation for data integration and exploration in translational medicine

Tese de doutoramento, Informática (Bioinformática), Universidade de Lisboa, Faculdade de Ciências, 2014

Information search in web archives

Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2014

A biosignal embedded system for physiological computing

Thesis submitted in the fulfilment of the requirements for the Degree of Master in Electronic and Telecomunications Engineering

Trabalho colaborativo entre professores para integração curricular no 1º ciclo do ensino básico: um contributo para melhoria das planificações pedagógicas em S.Tomé e Príncipe

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção do grau de mestre em Didáticas Integradas em Língua Portuguesa, Matemática, Ciências Naturais e Sociais

Formar o “leitor público”: contributos do teatro para o desenvolvimento da leitura em voz alta

Dissertação apresentada à Escola Superior de Educação de Lisboa para obtenção de grau de mestre em Educação Artística, na especialização de Teatro na Educação

A gestão do risco de liquidez e a concessão de crédito durante os períodos de crise

Dissertação de Mestrado Apresentada ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Contabilidade e Finanças, sob orientação do Doutor Mário Joel Matos Veiga de Oliveira Queirós.

Canada, a child's play

Science4you, a Portuguese developer, producer and seller of scientific and educational toys, leveraged the worldwide growth of this category to successfully expand its operations abroad. Following a recent entry into the United States market, the purpose of this report is to help the company define the next step in its international expansion. A customized scoring model, based on a set of relevant macro and micro-criteria was developed for Anglo-Saxon and Asian countries, yielding Canada as the market with the highest potential. The recommended entry mode is direct exporting via an independent distributor, being complemented with a financial and risk analysis.