Produção hospitalar : a importância da complexidade e da gravidade

RESUMO - A definição e medição da produção são questões centrais para a administração hospitalar. A produção hospitalar, quando se consideram os casos tratados, baseia-se em dois aspectos: a definição de sistemas de classificação de doentes como metodologia para identificar produtos e a criação de índices de casemix para se compararem esses mesmos produtos. Para a sua definição e implementação podem ser consideradas características relacionadas com a complexidade dos casos (atributo da oferta) ou com a sua gravidade (atributo da procura), ou ainda características mistas. Por sua vez, a análise do perfil e da política de admissões dos hospitais adquire um maior relevo no contexto de novas experiências previstas e em curso no SNS e da renovada necessidade de avaliação e regulação que daí decorrem. Neste estudo pretendeu-se discutir a metodologia para apuramento do índice de casemix dos hospitais, introduzindo- se a gravidade dos casos tratados como atributo relevante para a sua concretização. Assim, foi analisada uma amostra de 950 443 casos presentes na base de dados dos resumos de alta em 2002, tendo- -se dado particular atenção aos 31 hospitais posteriormente constituídos como SA. Foram considerados três índices de casemix: índice de complexidade (a partir do peso relativo dos DRGs), índice de gravidade (a partir da escala de mortalidade esperada do disease staging recalibrada para Portugal) e índice conjunto (média dos dois anteriores). Verificou-se que a análise do índice de complexidade, de gravidade e conjunto dá informações distintas sobre o perfil de admissões dos hospitais considerados. Os índices de complexidade e de gravidade mostram associações distintas às características dos hospitais e dos doentes tratados. Para além disso, existe uma diferença clara entre os casos com tratamento médico e cirúrgico. No entanto, para a globalidade dos hospitais analisados observou-se que os hospitais que tratam os casos mais graves tratam igualmente os mais complexos, tendo-se ainda identificado alguns hospitais em que tal não se verifica e, quando possível, apontado eventuais razões para esse comportamento.

Learning domain-specific sentiment lexicons with applications to recommender systems

Search is now going beyond looking for factual information, and people wish to search for the opinions of others to help them in their own decision-making. Sentiment expressions or opinion expressions are used by users to express their opinion and embody important pieces of information, particularly in online commerce. The main problem that the present dissertation addresses is how to model text to find meaningful words that express a sentiment. In this context, I investigate the viability of automatically generating a sentiment lexicon for opinion retrieval and sentiment classification applications. For this research objective we propose to capture sentiment words that are derived from online users’ reviews. In this approach, we tackle a major challenge in sentiment analysis which is the detection of words that express subjective preference and domain-specific sentiment words such as jargon. To this aim we present a fully generative method that automatically learns a domain-specific lexicon and is fully independent of external sources. Sentiment lexicons can be applied in a broad set of applications, however popular recommendation algorithms have somehow been disconnected from sentiment analysis. Therefore, we present a study that explores the viability of applying sentiment analysis techniques to infer ratings in a recommendation algorithm. Furthermore, entities’ reputation is intrinsically associated with sentiment words that have a positive or negative relation with those entities. Hence, is provided a study that observes the viability of using a domain-specific lexicon to compute entities reputation. Finally, a recommendation system algorithm is improved with the use of sentiment-based ratings and entities reputation.

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.