[Vaskuläre Dyspnoe: Lungenembolie]. / Vaskuläre Dyspnoe: Lungenembolie.

INTRODUCTION: In clinical practice, the differentiation of pulmonary embolism from other entities often remains difficult. Of utmost importance is the estimation of the pretest probability of the disease: predictive scoring systems and the use of clinical gestalt are equally useful tools. Exclusion or confirmation of the disease requires the rationale use of additional investigations (laboratory, imaging). In this article, we provide clinical engrams and outline our diagnostic algorithm. Based on the latest recommendations, we summarize the therapeutic approach for patients with pulmonary embolism. The importance of follow-up visits after the initial event is discussed in the last part. The assessment of risk factors promoting the development of venous thromboembolism is crucial for estimating the risk of recurrence. Excessive screening (thrombophilia testing or tumor investigations) are of minor relevance.

Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks.

We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved. We deploy heuristic algorithms and exhaustive ones to generate Bayesian networks among the monitored variables. The aim is to describe the relevant relationships between the variables, to discover and confirm the possible cause-effect relationships, to predict the fuel consumption dependent on the contextual conditions of traffic, and to enable an intervention analysis to be conducted on the variables so that our goals are achieved. We propose a validation technique using Bayesian networks based on Granger causality: it relies upon observations of the time series formed by successive values of the variables in time. We use the same method based on Granger causality to rank the Bayesian networks obtained as well. A comparison of the Bayesian networks discovered against the ground truth is proposed in a synthetic data set, specifically generated for this study: the results confirm the validity of the Bayesian networks that agree on most of the existing relationships.