Contract-program evaluation: Impact of the DRG classification system on hospitals' outputs

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Retrospective histopathological classification of 1,108 skin biopsies from patients clinically suspected of having leprosy from Bahia, Northeast Brazil

We report a retrospective histopathological classification carried out under laboratory conditions by the method of Ridley & Jopling of 1,108 skin biopsies from patients clinically suspected of having leprosy from Bahia, Northeast Brazil.

Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic.

Context classification for service robots

This dissertation presents a solution for environment sensing using sensor fusion techniques and a context/environment classification of the surroundings in a service robot, so it could change his behavior according to the different rea-soning outputs. As an example, if a robot knows he is outdoors, in a field environment, there can be a sandy ground, in which it should slow down. Contrariwise in indoor environments, that situation is statistically unlikely to happen (sandy ground). This simple assumption denotes the importance of context-aware in automated guided vehicles.

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.

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.

O DESENVOLVIMENTO OVOCITÁRIO EM Macrobrachium amazonicum(Heller, 1862)(CRUSTACEA: DECAPODA: PALAEMONIDAE), CAMARÃO DULCÍCOLA DA REGIÃO AMAZÔNICA

É descrito o desenvolvimento ovocitário em Macrobrachium amazonicum(Heller, 1862), um camarão dulcícola da família Palaemonidae da Amazônia Central. O seu desenvolvimento é semelhante ao de Macrobrachium acanthurus(Wiegmann, 1836). As fases iniciais do desenvolvimento ocupam a região intema do ovario, enquanto que as fases vitelogênica e madura situam-se perifericamente. São descritas ainda a morfologia dos folículos pós-ovulatórios e das células foliculares.

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.

Biologia reprodutiva do Camarão Macrobrachium brasiliense (Heller, 1862) (Crustacea: Decapoda: Palaemonidae) em Igarapés de terra firme da Amazônia Peruana

Foi estudada a biologia reprodutiva de Macrobrachium brasiliense (Decapoda: Palaemonidae) com base em 2604 indivíduos coletados entre outubro de 1994 e agosto de 1995, em dois igarapés de terra firme da estrada Iquitos-Nauta, Loreto (Amazônia peruana). A proporção sexual fêmea: macho foi de 1,7:1 sendo que as fêmeas foram um pouco mais abundantes que os machos durante todo o período de coleta. M. brasiliense apresentou dimorfismo sexual no comprimento cefalotorácico, sendo as fêmeas ligeiramente menores que os machos. A reprodução foi contínua, com o pico reprodutivo entre abril e julho. A fecundidade variou de 15 a 168 ovos por fêmea, observando-se uma relação significativa entre a fecundidade e o comprimento cefalotorácico e o peso. O volume do ovo não apresentou relação significativa com o comprimento cefalotorácico e a fecundidade.

Natural diet and feeding habits of a freshwater prawn (Macrobrachium carcinus: Crustacea, Decapoda) in the estuary of the Amazon River

Macrobrachium carcinus is a Brazilian native prawn with recognized potential for use in aquaculture activities. However, there is little information about the natural diet and feeding habits of this species. The aim of this study was the identification of the diet items of M. carcinus based on the analysis of the stomach contents. Specimens were collected in the Amazon River estuary between January 2009 and January 2010. The stomach analysis was carried out by using the frequency of occurrence (FO), methods of points (MP) and feeding index (FI). It was observed that prawns fed on detritus, animals and plant fragments as the most important food items. Sediment accounted for the main stomach content, accounting for 43.2% by the MP, 44.9% by FI and 100% by the FO. Sexual differences in feeding preferences were not found in this study, and seasonal differences in the frequency of items ingested by M. carcinus were not observed. The results indicated that M. carcinus can be considered omnivorous species, but with an important carnivorous component, similar to that found in other Macrobrachium species.

Reproductive aspects of Macrobrachium amazonicum (Decapoda: Palaemonidae) in the State of Amapá, Amazon River mouth

Macrobrachium amazonicum is an indigenous prawn vastly distributed in basins of South America, widely exploited by artisanal fisheries in northern and northeastern Brazil and, with great potential for aquaculture. This study aimed to investigate general aspects of population structure and reproductive characteristics (size at first maturity, fecundity and reproductive output) of M. amazonicum from two important areas to artisanal prawn fishing located at the mouth of the Amazon River, State of Amapá. The specimens were captured using 20 handcrafted traps called "matapi". A number of 5,179 prawns were captured, 2,975 females and 2,195 males resulting in 1.35:1 female to male ratio. Santana Island and Mazagão Velho showed females predominated in the population. A reproductive peak period was observed from January to April/2009 and in December/2010, coinciding with the period of higher rainfall. The recruitment peak occurred in June and July/2009. Egg-bearing females ranged in size (carapace length) from 11.10 to 29.6 mm. Fecundity increased with female size and reached up to 7,417 eggs. This amount of eggs is considered low if compared with other Macrobrachium estuarine species. Mean egg volume increased gradually from 0.121 to 0.24 mm³ during embryogenesis, representing 68.5% of overall increase from Stage I to Stage III. Eggs of M. amazonicum are small; this is typical for Macrobrachium species, which depends on brackish water to complete the larval development. Irrespective of female size, reproductive output of M. amazonicum varied between 4.8 and 21.85% of their body weight into eggs production.

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.