Browsing by Author "Oliveira, Alexandra"
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- Affinity coefficient for clustering autoregressive moving average modelsPublication . Nascimento, Ana Paula; Oliveira, Alexandra; Faria, Brígida Mónica; Pimenta, Rui; Vieira, Mónica; Prudêncio, Cristina; Nicolau, Helena BacelarIn various fields, such as economics, finance, bioinformatics, geology, and medicine, namely, in the cases of electroencephalogram, electrocardiogram, and biotechnology, cluster analysis of time series is necessary. The first step in cluster applications is to establish a similarity/dissimilarity coefficient between time series. This article introduces an extension of the affinity coefficient for the autoregressive expansions of the invertible autoregressive moving average models to measure their similarity between them. An application of the affinity coefficient between time series was developed and implemented in R. Cluster analysis is performed with the corresponding distance for the estimated simulated autoregressive moving average of order one. The primary findings indicate that processes with similar forecast functions are grouped (in the same cluster) as expected concerning the affinity coefficient. It was also possible to conclude that this affinity coefficient is very sensitive to the behavior changes of the forecast functions: processes with small different forecast functions appear to be well separated in different clusters. Moreover, if the two processes have at least an infinite number of π- weights with a symmetric signal, the affinity value is also symmetric.
- An approach for assessing the distribution of reporting delay in portuguese AIDS dataPublication . Oliveira, Alexandra; Gaio, Ana Rita; da Costa, Joaquim Pinto; Reis, Luís PauloHIV/AIDS epidemic is an important public health problem. The burden of the epidemic is estimated from surveillance systems data. The collected information is incomplete, making the estimation a challenging task and the reported trends often biased. The most common incomplete-data problems, in this kind of data, are due to under-diagnosis and reporting delays, mainly in the most recent years. This is a classical problem for imputation methodologies. In this paper we study the distribution of AIDS reporting delays through a mix approach, combining longitudinal K-means with the generalized least squares method. While the former identifies homogeneous delay patterns, the latter estimated longitudinal regression curves. We found that a 2-cluster structure is appropriated to accommodate the heterogeneity in reporting delay on HIV/AIDS data and that the corresponding estimated delay curves are almost stationary over time.
- An approach to assess quality of life through biometric monitoring in cancer patientsPublication . Silva, Eliana; Aguiar, Joyce; Oliveira, Alexandra; Faria, Brígida Mónica; Reis, Luís Paulo; Carvalho, Victor; Gonçalves, Joaquim; Oliveira e Sá, JorgeCancer is a serious disease that causes significant disability and suffering, so naturally Health Related Quality of Life (HRQoL) is a major concern of patients, families and clinicians. This paper intends to relate biometric indices, in terms of HRV metrics, with self-perceived HRQoL from patients with lymphoma. Patients (N = 12) answered FACT questionnaire and used a smartband that collected biometrical data in real-time along the chemotherapy treatment. Our results revealed that Physical Well-Being, Total, Lymphoma subscale and FACT-Lym Trial Outcome domains seem to have a similar pattern that HRV metrics across the treatment cycles. In specific, the FACT domains and the HRV metrics have the lowest average levels on the first cycle and seem to increase along the following cycles (3rd and 6th cycles). This approach of continuous assessment of HRQoL will enable a better accuracy and more supported clinical decision.
- Antihypertensives knowledge – assessment in higher education students through digital game-based learningPublication . Gonçalves, Helena; Capitão, Romana; Cruz, Agostinho; Oliveira, Alexandra; Oliveira, Ana Isabel; Pinho, Cláudia; Borges, Janete; Oliveira, Rita FerrazHypertension has been described as the most prevalent risk factor for cardiovascular diseases, which are the main cause of death worldwide. In Portugal, in 2015, its prevalence was of 36%. This pathology’s therapeutic success depends upon a good knowledge about the therapeutic alternatives available.There is, therefore, imperative to insure that the learning process is efficient. In order to improve it, in a more appealing and effective way, there has been an increase in the employ of digital tools.
- Assessing daily activities using a PPG sensor embedded in a wristband-type activity trackerPublication . Oliveira, Alexandra; Aguiar, Joyce; Silva, Eliana; Faria, Brígida Mónica; Gonçalves, Helena; Teófilo, Luís; Gonçalves, Joaquim; Carvalho, Victor; Cardoso, Henrique Lopes; Reis, Luís Paulo; Faria, Brigida Monica; Oliveira, AlexandraDue to the technological evolution on wearable devices, biosignals, such as inter-cardiac beat interval (RR) time series, are being captured in a noncontrolled environment. These RR signals, derived from photoplethysmography (PPG), enable health status assessment in a more continuous, non-invasive, nonobstructive way, and fully integrated into the individual’s daily activity. However PPG is vulnerable to motion artefacts, which can affect the accuracy of the estimated neurophysiological markers. This paper introduces a method for motion artefact characterization in terms of location and relative variation parameters obtained in different common daily activities. The approach takes into consideration interindividual variability. Data was analyzed throughout related-samples Friedman’s test, followed by pairwise comparison with Wilcoxon signed-rank tests with a Bonferroni correction. Results showed that movement, involving only arms, presents more variability in terms of the two analyzed parameters.
- Assessing sleep quality of professional drivers: an analysis based on self-perceived and sleep companions' feedbackPublication . Lopes, T.; Faria, Brigida Monica; Oliveira, Alexandra; Pimenta, Rui; Reis, L. P.Portugal has been ranked as the fourth European country with the highest incidence of falling asleep while driving. The quality of sleep comprises both quantitative aspects, such as sleep duration and sleep latency, and qualitative aspects, such as mood and health status. Neglecting the quality and quantity of sleep can result in fatigue, affecting multiple aspects of safe driving, such as attentiveness to the road. Although quantitative measures of sleep are easy to assess, evaluating subjective aspects of sleep is more challenging. Poor sleep quality and habits were the most commonly cited reasons for falling asleep at the wheel. Given the high prevalence of road accidents in Portugal and the significant impact of sleep quality on driving safety, there is a need for comprehensive research on the sleep quality of professional drivers. Adult sleep is often a shared activity between sleep companions, making it a crucial aspect to investigate for a better understanding of sleep perceptions. The main objective of this study is to analyze the sleep quality of a population of Portuguese professional drivers and compare it with the responses given by their sleep companions.
- Biometrics and quality of life of lymphoma patients: A longitudinal mixed‐model approachPublication . Oliveira, Alexandra; Silva, Eliana; Aguiar, Joyce; Faria, Brigida Monica; Reis, Luís Paulo; Cardoso, Henrique; Gonçalves, Joaquim; Sá, Jorge Oliveira e Carvalho; Victor, Marques HerlanderKnowledge Engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health‐related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato‐oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real‐time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability.
- Comparing time series forecasting models for health indicators: A clustering analysis approachPublication . Vinhal, Cláudia; Oliveira, Alexandra; Faria, Brígida; Nascimento, Ana Paula; Pimenta, Rui; Oliveira, Alexandra; Faria, Brigida Monica; Pimenta, RuiTime series are the sequence of observations ordered by equal time intervals, crucial for understanding causality, trends, and forecasts. Its analysis can be applied to several areas, such as engineering, finance, and health (1,2). One problem with the time series study is clustering, mainly understanding when two parametric time series are considered similar (3). The sum of mortality and morbidity, referred to as “Burden of Disease”, is measured by a metric called “Disability Adjusted Life Years” (DALYs) (4). These indicators are direct measures of health care needs, reflecting the global burden of disease in the population, and are crucial for public health study and surveillance (5). DALYs can be represented by Autoregressive Integrated Moving Averages (ARIMA) models, and in this context understanding clusters is crucial. The primary goal is to compare different distance measures between ARIMA processes when used in clustering techniques. The study begins by exploring the temporal characteristics of DALYs, highlighting underlying patterns and trends. Then, ARIMA models are applied to represent and describe the time series. It’s on this representation of the time series that the Piccolo, the Maharaj, and the LPC distance measures are applied to use clustering techniques and identify clusters. Additionally, 8 distinct cluster validation metrics are used. Specific to 48 European countries, the results show that the choice of distance measure can greatly influence clustering outcomes and the number of clusters formed. While certain methods revealed geographic patterns, other factors, such as cultural or economic similarities, also influence cluster formation. These insights contribute to advancing the field of public health surveillance and intervention, ultimately aiming to alleviate the global burden of disease. This study offers insights into applying ARIMA processes in clustering techniques for analysing temporal health data. By comparing different distance measures, this research improves our understanding of underlying patterns and trends in health indicators over time.
- Data Mining in HIV-AIDS Surveillance SystemPublication . Oliveira, Alexandra; Faria, Brigida Monica; Gaio, Rita; Reis, Luis PauloThe Human Immunodeficiency Virus (HIV) is an infectious agent that attacks the immune system cells. Without a strong immune system, the body becomes very susceptible to serious life threatening opportunistic diseases. In spite of the great progresses on medication and prevention over the last years, HIV infection continues to be a major global public health issue, having claimed more than 36 million lives over the last 35 years since the recognition of the disease. Monitoring, through registries, of HIV-AIDS cases is vital to assess general health care needs and to support long-term health-policy control planning. Surveillance systems are therefore established in almost all developed countries. Typically, this is a complex system depending on several stakeholders, such as health care providers, the general population and laboratories, which challenges an efficient and effective reporting of diagnosed cases. One issue that often arises is the administrative delay in reports of diagnosed cases. This paper aims to identify the main factors influencing reporting delays of HIV-AIDS cases within the portuguese surveillance system. The used methodologies included multilayer artificial neural networks (MLP), naive bayesian classifiers (NB), support vector machines (SVM) and the k-nearest neighbor algorithm (KNN). The highest classification accuracy, precision and recall were obtained for MLP and the results suggested homogeneous administrative and clinical practices within the reporting process. Guidelines for reductions of the delays should therefore be developed nationwise and transversally to all stakeholders.
- Data quality miningPublication . Oliveira, Alexandra; Gaio, Rita; Baylina, Pilar; Rebelo, Carlos; Reis, Luís PauloWe are living in a world of information abundance, surplus, and access. We have technologies to acquire any type of information but we still face the challenge of extracting the underlying valuable knowledge. Data analyses and mining processes may be severely impaired whenever data are corrupted by noise, ambiguity and distortions. This paper aims to provide a systematic procedure for data cleaning in single files data sources without schema that may be corrupted by the most common data problems. The methodology is guided by the dimensions of data quality standards and focuses on the goal of performing reasonable posterior statistical analyses.
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