3 resultados para Spot sizes

em Université de Lausanne, Switzerland


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The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

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To assess the impact of admission to different hospital types on early and 1-year outcomes in patients with acute coronary syndrome (ACS). Between 1997 and 2009, 31 010 ACS patients from 76 Swiss hospitals were enrolled in the AMIS Plus registry. Large tertiary institutions with continuous (24 hour/7 day) cardiac catheterisation facilities were classified as type A hospitals, and all others as type B. For 1-year outcomes, a subgroup of patients admitted after 2005 were studied. Eleven type A hospitals admitted 15987 (52%) patients and 65 type B hospitals 15023 (48%) patients. Patients admitted into B hospitals were older, more frequently female, diabetic, hypertensive, had more severe comorbidities and more frequent non-ST segment elevation (NSTE)-ACS/unstable angina (UA). STE-ACS patients admitted into B hospitals received more thrombolysis, but less percutaneous coronary intervention (PCI). Crude in-hospital mortality and major adverse cardiac events (MACE) were higher in patients from B hospitals. Crude 1-year mortality of 3747 ACS patients followed up was higher in patients admitted into B hospitals, but no differences were found for MACE. After adjustment for age, risk factors, type of ACS and comorbidities, hospital type was not an independent predictor of in-hospital mortality, in-hospital MACE, 1-year MACE or mortality. Admission indicated a crude outcome in favour of hospitalisation during duty-hours while 1-year outcome could not document a significant effect. ACS patients admitted to smaller regional Swiss hospitals were older, had more severe comorbidities, more NSTE-ACS and received less intensive treatment compared with the patients initially admitted to large tertiary institutions. However, hospital type was not an independent predictor of early and mid-term outcomes in these patients. Furthermore, our data suggest that Swiss hospitals have been functioning as an efficient network for the past 12 years.