Stefan Visscher

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Diagnosing ventilator-associated pneumonia in mechanically ventilated patients in intensive care units is currently seen as a clinical challenge. The difficulty in diagnosing ventilator-associated pneumonia stems from the lack of a simple yet accurate diagnostic test. To assist clinicians in diagnosing and treating patients with pneumonia , a(More)
BACKGROUND Inappropriate medication prescription is a common cause of preventable adverse drug events among elderly persons in the primary care setting. OBJECTIVE The aim of this systematic review is to quantify the extent of inappropriate prescription to elderly persons in the primary care setting. METHODS We systematically searched Ovid-Medline and(More)
OBJECTIVE Appropriate antimicrobial treatment of infections in critically ill patients should be started as soon as possible, as delay in treatment may reduce a patient's prognostic outlook considerably. Ventilator-associated pneumonia (VAP) occurs in patients in intensive care units who are mechanically ventilated and is almost always preceded by(More)
OBJECTIVE To determine the diagnostic performance of a Bayesian Decision-Support System (BDSS) for ventilator-associated pneumonia (VAP). DESIGN A previously developed BDSS, automatically obtaining patient data from patient information systems, provides likelihood predictions of VAP. In a prospectively studied cohort of 872 ICU patients, VAP was diagnosed(More)
OBJECTIVE Large health care datasets normally have a hierarchical structure, in terms of levels, as the data have been obtained from different practices, hospitals, or regions. Multilevel regression is the technique commonly used to deal with such multilevel data. However, for the statistical analysis of interactions between entities from a domain,(More)
BACKGROUND We previously validated a Bayesian network (BN) model for diagnosing ventilator-associated pneumonia (VAP). Here, we report on the performance of the model to predict microbial causes of VAP and to select antibiotics. METHODS Pathogens were grouped into seven categories based upon the antibiotic susceptibility and epidemiological(More)
Dynamic Bayesian networks are a special type of Bayesian networks, which explicitly deal with the dimension of time. They are distinguished into repetitive and non-repetitive networks. Repetitive networks have the same set of random (statistical) variables and independence relations at each time step, whereas in non-repetitive networks the set of random(More)
OBJECTIVES Although the course of single diseases can be studied using traditional epidemiologic techniques, these methods cannot capture the complex joint evolutionary course of multiple disorders. In this study, multilevel temporal Bayesian networks were adopted to study the course of multimorbidity in the expectation that this would yield new clinical(More)
Disease processes in patients are temporal in nature and involve uncertainty. It is necessary to gain insight into these processes when aiming at improving the diagnosis, treatment and prognosis of disease in patients. One way to achieve these aims is by explicitly modelling disease processes; several researchers have advocated the use of dynamic Bayesian(More)