Bernardo Gonçalves

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In this paper we test the hypothesis that a domain reference ontology of the electrocardiogram (ECG) can be employed in an effective manner to achieve semantic integration between ECG data standards. Several standardization initiatives, namely AHA/MIT-BIH (Physionet), SCP-ECG and HL7 aECG, have led to heterogeneous conceptualizations of the ECG domain. We(More)
The latest population-based studies in the medical literature worldwide indicate that acute myocardial infarction (AMI) patients still experience prolonged delay to be rescued, which often results in morbidity and mortality. This paper reports from a technological standpoint a teleconsultation and monitoring system named AToMS. This system addresses the(More)
The latest computer and communication technologies in combination with an enhanced ECG analysis system can be used to improve cardiac patient's follow-up out-of-hospital. In this way, real-time transmission of the so-called ambulatory electrocardiogram (AECG) to a remote health application with awareness of the patient's context can support decision making(More)
Computational technologies have been increasingly explored to make biomedical knowledge and data more accessible for human understanding, comparison, analysis and communication. In this context, ontology has been recognized in the bioinformatics literature as a suitable technique for advancing knowledge and data representations in Biomedicine. Moreover,(More)
The ambulatory electrocardiogram (AECG) can be acquired and transmitted through mobile and wireless technologies and devices to foster heart’s telemonitoring anytime, anywhere. This sort of service is purposeful when combined with ECG analysis systems and infrastructural support for providing contextaware services. Such setting makes efficient emergency(More)
The sheer scale of high-resolution raw data generated by simulation has motivated nonconventional approaches for data exploration, referred to as immersive and in situ query processing. Another step toward supporting scientific progress is to enable data-driven hypothesis management and predictive analytics out of simulation results. The authors of this(More)
As the problems of interest in large-scale science are ever more complex or intertwined , scientists can benefit from machinery to manage hypotheses and their interconnection in the context of a large-scale research project. A partial order is assumed to model hypothesis interconnectivity, meaning that hypotheses may be 'based on or equal to' one another.(More)
In view of the paradigm shift that makes science ever more data-driven, in this paper we consider determinis-tic scientific hypotheses as uncertain data. In the form of mathematical equations, hypotheses symmetrically relate aspects of the studied phenomena. For computing predictions, however, deterministic hypotheses are used asymmetrically as functions.(More)
One of the main issues that inhibit the development of context-aware mobile applications is the lack of systematic methods for sensor data acquisition. This lack, however, is a result of the diversity of sensor data and its acquisition devices. In face of this, there is a need for general engineering solutions in order to address the common sensor data(More)
G635m Managing large-scale scientific hypotheses as uncertain and probabilis-Dedicatory To my parents Tania and Francisco, and to my special love, Marcelle, for being an island of certainty in an uncertain world. I would like to express my gratitude to my advisor Fabio Porto for the gift of the challenging topic of this thesis, so special to me. I am(More)