Prediction of road accidents: A Bayesian hierarchical approach.

In this paper a novel methodology for the prediction of the occurrence of road accidents is presented. The methodology utilizes a combination of three statistical methods: (1) gamma-updating of the occurrence rates of injury accidents and injured road users, (2) hierarchical multivariate Poisson-lognormal regression analysis taking into account correlations amongst multiple dependent model response variables and effects of discrete accident count data e.g. over-dispersion, and (3) Bayesian inference algorithms, which are applied by means of data mining techniques supported by Bayesian Probabilistic Networks in order to represent non-linearity between risk indicating and model response variables, as well as different types of uncertainties which might be present in the development of the specific models. Prior Bayesian Probabilistic Networks are first established by means of multivariate regression analysis of the observed frequencies of the model response variables, e.g. the occurrence of an accident, and observed values of the risk indicating variables, e.g. degree of road curvature. Subsequently, parameter learning is done using updating algorithms, to determine the posterior predictive probability distributions of the model response variables, conditional on the values of the risk indicating variables. The methodology is illustrated through a case study using data of the Austrian rural motorway network. In the case study, on randomly selected road segments the methodology is used to produce a model to predict the expected number of accidents in which an injury has occurred and the expected number of light, severe and fatally injured road users. Additionally, the methodology is used for geo-referenced identification of road sections with increased occurrence probabilities of injury accident events on a road link between two Austrian cities. It is shown that the proposed methodology can be used to develop models to estimate the occurrence of road accidents for any road network provided that the required data are available.

2D image registration in CT images using radial image descriptors.

Registering CT scans in a body atlas is an important technique for aligning and comparing different CT scans. It is also required for navigating automatically to certain regions of a scan or if sub volumes should be identified automatically. Common solutions to this problem employ landmark detectors and interpolation techniques. However, these solutions are often not applicable if the query scan is very small or consists only of a single slice. Therefore, the research community proposed methods being independent from landmark detectors which are using imaging techniques to register the slices in a generalized height scale. In this paper, we propose an improved prediction method for registering single slices. Our solution is based on specialized image descriptors and instance-based learning. The experimental evaluation shows that the new method improves accuracy and stability of comparable registration methods by using only a single CT slice is required for the registration.

[Cost of care for depressive disorders in primary and specialized care in Germany]. / Behandlungskosten von Patienten mit Depressionsdiagnose in haus- und fachärztlicher Versorgung in Deutschland.

Service utilization and total direct cost of care was assessed in 270 patients suffering from depressive disorder. Patients were recruited from primary care physicians or family doctors (n = 43) or psychiatrists (n = 23) in office practice, from three different regions in Germany (county of Düren and city of Aachen, Lörrach-county, city of Munich). A detailed catalogue of unit costs (including inpatient, outpatient and rehabilitative services) was used for calculating total cost of care on an individual basis. Service utilization and costs referred to 2001. Mean cost of total medical care of the study patients was euro 3849 (excluding cost of drugs for physical illness). The cost for treating depressive disorders and additional psychiatric co-morbidity (which is included into the total cost of care) was euro 2073 per patient and year. When cross-checking with ICD-10 criteria for depressive disorders, the original diagnosis by family doctors or psychiatrists could be confirmed in 186 patients of the total sample (n = 270), suggesting that there is a high amount of falsely diagnosed patients in primary and specialized care of depressive patients in Germany. Direct cost of the 186 confirmed patients was higher (total care cost: euro 4715, cost for treatment of depression and psychiatric co-morbidity: euro 2541) than in the total group and should be considered as reference cost, when discussing cost of care in depressive patients in Germany. Results suggest to analyse cost of care in depressive patients further and to discuss a more efficient allocation of health budgets in the field.