A Classificação Internacional de Funcionalidade, Incapacidade e Saúde : um modelo multidimensional e multideterminado da funcionalidade em idosos assistidos na Rede

RESUMO: Os estudos sobre a funcionalidade da população idosa têm uma representação importante naquilo que é o atual conhecimento da demografia do mundo. Portugal posiciona-se e perspetiva-se como pertencendo aos países mais envelhecidos, possuindo uma rede de cuidados pós-agudos – a Rede Nacional de Cuidados Continuados Integrados (RNCCI)– que assiste uma parcela importante dessa população. Os aspetos conceptuais da funcionalidade de acordo com a OMS e operacionalizados pela Classificação Internacional de Funcionalidade (CIF), não mereceram até agora suficiente aplicabilidade no nosso país, inviabilizando a possibilidade de oferecermos contributos para a sua operacionalização. Da mesma forma, também os Core Sets da Classificação não têm sido sujeitos a processos de validação que contemplem amostras portuguesas, mantendo-se desconhecimento da especificidade dos fatores contextuais na nossa população. O presente estudo tem como objetivos conhecer a evolução da funcionalidade dos idosos assistidos na RNCCI na região do Algarve nas unidades de convalescença e média duração, validar o Core Set Geriátrico da OMS e propor uma versão abreviada da sua modalidade abrangente, no contexto destes cuidados. A amostra constituída por 451 idosos, dos quais 62,1% eram mulheres, revelou na pré-morbilidade níveis favoráveis de funcionalidade, com exceção para as Atividades Domésticas. Contudo, os mais idosos (≥ 85 anos), os indivíduos sem escolaridade, as mulheres e os viúvos/solteiros apresentaram mais casos desfavoráveis quando comparados com os seus pares. Na evolução da funcionalidade observámos melhorias significativas em todos os domínios avaliados, com diferenças relativamente à idade e à escolaridade; apesar dos resultados positivos os mais idosos e os indivíduos sem escolaridade apresentaram níveis inferiores de evolução. No entanto, a funcionalidade alcançada revelou ficar com resultados significativamente inferiores na comparação com aquela que os indivíduos possuíam na pré-morbilidade. Os modelos de regressão revelaram que as Funções Mentais, a Perceção do Estado de Saúde e a atividade Usar o Telefone, foram as variáveis que melhor explicaram os outcomes da funcionalidade alcançada. A validação do Core Set Geriátrico foi possível na maioria das categorias, sendo que foi no componente das Funções do Corpo onde esse processo revelou maior fragilidade. As Funções Neuromusculoesqueléticas e Relacionadas com o Movimento foram aquelas que registaram em ambos os momentos avaliativos frequências mais elevadas de deficiência, enquanto no componente Atividades & Participação isso ocorreu na atividade Utilização dos Movimentos Finos da Mão. Os capítulos Apoios e Relacionamentos e Atitudes foram considerados os Fatores Ambientais mais Facilitadores mas também com maior impacto Barreira. A proposta para o Core Set Geriátrico Abreviado resultou das categorias independentes que explicaram os modelos da funcionalidade alcançada e cujo resultado engloba um conjunto de 27 categorias, com um enfoque importante no componente Atividades/Participação de onde se destacam os domínios da Mobilidade e dos Auto Cuidados. A funcionalidade dos indivíduos e das populações deve ser considerada uma variável incontornável da Saúde Pública, cuja avaliação deve refletir uma abordagem biopsicossocial, apoiada na Classificação Internacional de Funcionalidade. A operacionalização da Classificação a partir dos Core Sets necessita de pesquisa mais aprofundada relativamente às caraterísticas psicométricas dos seus qualificadores e dos seus processos de validação.-----------ABSTRACT: The studies about the functioning of the elderly play an important role on what the present knowledge of the demography in the world is. Portugal figures high on the most aged countries, having a network of post-acute care - the National Network of Integrated Continuous Care (RNCCI) - which assists a large part of that population. The conceptual aspects of functioning according to WHO and operated by the International Classification of Functioning (ICF), have been insufficiently addressed concerning its adequate applicability in our country, hindering the contributions of its operation. In the same way, also the Core Sets of the Classification have not been subjected to validation procedures that include portuguese samples, keeping the unawareness of specificity of the contextual factors in our population. The objectives of the present study were to know the evolution of the functioning of the elderly assisted in the RNCCI in the Algarve region in units of convalescence and average duration, validate the WHO Geriatric Core Set and propose an abridged version of this comprehensive core set in this healthcare context. The sample was composed by 451 elderly people, of which 62.1% were women, they showed favourable levels in functioning in the pre-morbid state, except for Domestic Activities. However, the oldest (≥ 85 years), the individuals with no education, women and widowed/ unmarried showed more unfavourable cases when compared to their peers. In the evolution of functioning we observed significant improvements in all domains assessed, with diferences with respect to age and education. In spite of positive results, the oldest and the individuals with no education showed lower levels of evolution. However, the functioning achieved showed significantly lower results when compared to the those observed in pre-morbidity state. Regression models reveal that Mental Functions, the Perceived Health Status and the Use of the Phone activity, were the variables that better explain the functioning of the outcomes achieved. The validation of the Geriatric Core Set of ICF was possible in most categories, and Body Functions was the component where this process showed greatest weakness. Neuromusculoskeletal and Movement-Related Functions experienced in both evaluation times with higher rates of disability, while in the Activities & Participation component this occurred in the Fine Hand Use activity. The Support and Relationships and Attitudes chapters were considered the Environmental Factors most Facilitators but also with greater impact Barrier. The proposal for the Brief Geriatric Core Set has resulted from the independent categories that explained the regression models of functioning and includes a set of 27 categories, with na important emphasis on Activities & Participation component where we can highlight the areas of Mobility and Self Care domains. The functioning of individuals and populations should be considered as an unavoidable variable of Public Health, of which the assessment should reflect a biopsychosocial approach, based on the International Classification of Functioning. The operationalization of the Classification from the Core Sets requires further research regarding the psychometric characteristics of their qualifiers and their validation procedure.

Pair trading: Clustering based on principal component analysis

This study focuses on the implementation of several pair trading strategies across three emerging markets, with the objective of comparing the results obtained from the different strategies and assessing if pair trading benefits from a more volatile environment. The results show that, indeed, there are higher potential profits arising from emerging markets. However, the higher excess return will be partially offset by higher transaction costs, which will be a determinant factor to the profitability of pair trading strategies. Also, a new clustering approach based on the Principal Component Analysis was tested as an alternative to the more standard clustering by Industry Groups. The new clustering approach delivers promising results, consistently reducing volatility to a greater extent than the Industry Group approach, with no significant harm to the excess returns.

Using the International Classification of Functioning, Disability and Health as a tool for analysis of the effect of physical therapy on spasticity in HAM/TSP patients

INTRODUCTION: This study aimed to evaluate spasticity in human T-lymphotropic virus type 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) patients before and after physical therapy using the International Classification of Functioning, Disability and Health (ICF). METHODS: Nine subjects underwent physical therapy. Spasticity was evaluated using the Modified Ashworth Scale. The obtained scores were converted into ICF body functions scores. RESULTS: The majority of subjects had a high degree of spasticity in the quadriceps muscles. According to the ICF codes, the spasticity decreased after 20 sessions of physical therapy. CONCLUSIONS: The ICF was effective in evaluating spasticity in HAM/TSP patients.

Electrohysterogram signal component cataloging with spectral and time-frequency methods

The Electrohysterogram (EHG) is a new instrument for pregnancy monitoring. It measures the uterine muscle electrical signal, which is closely related with uterine contractions. The EHG is described as a viable alternative and a more precise instrument than the currently most widely used method for the description of uterine contractions: the external tocogram. The EHG has also been indicated as a promising tool in the assessment of preterm delivery risk. This work intends to contribute towards the EHG characterization through the inventory of its components which are: • Contractions; • Labor contractions; • Alvarez waves; • Fetal movements; • Long Duration Low Frequency Waves; The instruments used for cataloging were: Spectral Analysis, parametric and non-parametric, energy estimators, time-frequency methods and the tocogram annotated by expert physicians. The EHG and respective tocograms were obtained from the Icelandic 16-electrode Electrohysterogram Database. 288 components were classified. There is not a component database of this type available for consultation. The spectral analysis module and power estimation was added to Uterine Explorer, an EHG analysis software developed in FCT-UNL. The importance of this component database is related to the need to improve the understanding of the EHG which is a relatively complex signal, as well as contributing towards the detection of preterm birth. Preterm birth accounts for 10% of all births and is one of the most relevant obstetric conditions. Despite the technological and scientific advances in perinatal medicine, in developed countries, prematurity is the major cause of neonatal death. Although various risk factors such as previous preterm births, infection, uterine malformations, multiple gestation and short uterine cervix in second trimester, have been associated with this condition, its etiology remains unknown [1][2][3].

Evaluation of the World Health Organization 2009 classification of dengue severity in autopsied individuals, during the epidemics of 2011 and 2012 in Brazil

Abstract: INTRODUCTION: The dengue classification proposed by the World Health Organization (WHO) in 2009 is considered more sensitive than the classification proposed by the WHO in 1997. However, no study has assessed the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue. In the present study, we evaluated the ability of the WHO 2009 classification to identify dengue deaths among autopsied individuals suspected of having dengue in Northeast Brazil, where the disease is endemic. METHODS: This retrospective study included 121 autopsied individuals suspected of having dengue in Northeast Brazil during the epidemics of 2011 and 2012. All the autopsied individuals included in this study were confirmed to have dengue based on the findings of laboratory examinations. RESULTS: The median age of the autopsied individuals was 34 years (range, 1 month to 93 years), and 54.5% of the individuals were males. According to the WHO 1997 classification, 9.1% (11/121) of the cases were classified as dengue hemorrhagic fever (DHF) and 3.3% (4/121) as dengue shock syndrome. The remaining 87.6% (106/121) of the cases were classified as dengue with complications. According to the 2009 classification, 100% (121/121) of the cases were classified as severe dengue. The absence of plasma leakage (58.5%) and platelet counts <100,000/mm3 (47.2%) were the most frequent reasons for the inability to classify cases as DHF. CONCLUSIONS: The WHO 2009 classification is more sensitive than the WHO 1997 classification for identifying dengue deaths among autopsied individuals suspected of having dengue.

Analysis of classification algorithms for crop detection using LANDSAT 8 images

Remote sensing - the acquisition of information about an object or phenomenon without making physical contact with the object - is applied in a multitude of different areas, ranging from agriculture, forestry, cartography, hydrology, geology, meteorology, aerial traffic control, among many others. Regarding agriculture, an example of application of this information is regarding crop detection, to monitor existing crops easily and help in the region’s strategic planning. In any of these areas, there is always an ongoing search for better methods that allow us to obtain better results. For over forty years, the Landsat program has utilized satellites to collect spectral information from Earth’s surface, creating a historical archive unmatched in quality, detail, coverage, and length. The most recent one was launched on February 11, 2013, having a number of improvements regarding its predecessors. This project aims to compare classification methods in Portugal’s Ribatejo region, specifically regarding crop detection. The state of the art algorithms will be used in this region and their performance will be analyzed.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.

Safety classification using GIS in decision-making process to define priority road interventions

Given the current economic situation of the Portuguese municipalities, it is necessary to identify the priority investments in order to achieve a more efficient financial management. The classification of the road network of the municipality according to the occurrence of traffic accidents is fundamental to set priorities for road interventions. This paper presents a model for road network classification based on traffic accidents integrated in a geographic information system. Its practical application was developed through a case study in the municipality of Barcelos. An equation was defined to obtain a road safety index through the combination of the following indicators: severity, property damage only and accident costs. In addition to the road network classification, the application of the model allows to analyze the spatial coverage of accidents in order to determine the centrality and dispersion of the locations with the highest incidence of road accidents. This analysis can be further refined according to the nature of the accidents namely in collision, runoff and pedestrian crashes.

Refinement and variability techniques in model transformation of software requirements

Tese de Doutoramento em Tecnologias e Sistemas de Informação

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

Monovarietal extra-virgin olive oil classification: a fusion of human sensory attributes and an electronic tongue

Olive oil quality grading is traditionally assessed by human sensory evaluation of positive and negative attributes (olfactory, gustatory, and final olfactorygustatory sensations). However, it is not guaranteed that trained panelist can correctly classify monovarietal extra-virgin olive oils according to olive cultivar. In this work, the potential application of human (sensory panelists) and artificial (electronic tongue) sensory evaluation of olive oils was studied aiming to discriminate eight single-cultivar extra-virgin olive oils. Linear discriminant, partial least square discriminant, and sparse partial least square discriminant analyses were evaluated. The best predictive classification was obtained using linear discriminant analysis with simulated annealing selection algorithm. A low-level data fusion approach (18 electronic tongue signals and nine sensory attributes) enabled 100 % leave-one-out cross-validation correct classification, improving the discrimination capability of the individual use of sensor profiles or sensory attributes (70 and 57 % leave-one-out correct classifications, respectively). So, human sensory evaluation and electronic tongue analysis may be used as complementary tools allowing successful monovarietal olive oil discrimination.

Land cover classification of Lago Grande de Curuai floodplain (Amazon, Brazil) using multi-sensor and image fusion techniques

Given the limitations of different types of remote sensing images, automated land-cover classifications of the Amazon várzea may yield poor accuracy indexes. One way to improve accuracy is through the combination of images from different sensors, by either image fusion or multi-sensor classifications. Therefore, the objective of this study was to determine which classification method is more efficient in improving land cover classification accuracies for the Amazon várzea and similar wetland environments - (a) synthetically fused optical and SAR images or (b) multi-sensor classification of paired SAR and optical images. Land cover classifications based on images from a single sensor (Landsat TM or Radarsat-2) are compared with multi-sensor and image fusion classifications. Object-based image analyses (OBIA) and the J.48 data-mining algorithm were used for automated classification, and classification accuracies were assessed using the kappa index of agreement and the recently proposed allocation and quantity disagreement measures. Overall, optical-based classifications had better accuracy than SAR-based classifications. Once both datasets were combined using the multi-sensor approach, there was a 2% decrease in allocation disagreement, as the method was able to overcome part of the limitations present in both images. Accuracy decreased when image fusion methods were used, however. We therefore concluded that the multi-sensor classification method is more appropriate for classifying land cover in the Amazon várzea.

Reconstructing historical forest cover change in the Lower Amazon floodplains using the LandTrendr algorithm

ABSTRACTThe Amazon várzeas are an important component of the Amazon biome, but anthropic and climatic impacts have been leading to forest loss and interruption of essential ecosystem functions and services. The objectives of this study were to evaluate the capability of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm to characterize changes in várzeaforest cover in the Lower Amazon, and to analyze the potential of spectral and temporal attributes to classify forest loss as either natural or anthropogenic. We used a time series of 37 Landsat TM and ETM+ images acquired between 1984 and 2009. We used the LandTrendr algorithm to detect forest cover change and the attributes of "start year", "magnitude", and "duration" of the changes, as well as "NDVI at the end of series". Detection was restricted to areas identified as having forest cover at the start and/or end of the time series. We used the Support Vector Machine (SVM) algorithm to classify the extracted attributes, differentiating between anthropogenic and natural forest loss. Detection reliability was consistently high for change events along the Amazon River channel, but variable for changes within the floodplain. Spectral-temporal trajectories faithfully represented the nature of changes in floodplain forest cover, corroborating field observations. We estimated anthropogenic forest losses to be larger (1.071 ha) than natural losses (884 ha), with a global classification accuracy of 94%. We conclude that the LandTrendr algorithm is a reliable tool for studies of forest dynamics throughout the floodplain.

Deteção remota na avaliação das alterações da cobertura do solo no Parque Nacional da Peneda-Gerês

Dissertação de mestrado integrado em Engenharia Civil