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Driver distraction has become a leading cause of motor-vehicle crashes. Although visual and cognitive distraction has been studied extensively, relatively little research has addressed their combined effects on drivers' behavior. To fill this gap, a medium-fidelity simulator study examined the driver behavior before, during and after three types of(More)
OBJECTIVE In this study, the authors used algorithms to estimate driver distraction and predict crash and near-crash risk on the basis of driver glance behavior using the data set of the 100-Car Naturalistic Driving Study. BACKGROUND Driver distraction has been a leading cause of motor vehicle crashes, but the relationship between distractions and crash(More)
PURPOSE Medication errors remain a threat to patient safety. Therefore, the purpose of this study was to determine the relationships among characteristics of the nursing practice environment, nurse staffing levels, nurses' error interception practices, and rates of nonintercepted medication errors in acute care hospitals. DESIGN This study, using a(More)
BACKGROUND Although nurse staffing has been found to be related to patient mortality, there has been limited study of the independent effect of work schedules on patient care outcomes. OBJECTIVE To determine if, in hospitals where nurses report more adverse work schedules, there would be increased patient mortality, controlling for staffing. METHODS A(More)
MOTIVATION The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differentiation approaches can reduce the dimensions of data, help to remove redundant genes and noises, and highlight the most relevant genes that are major players in the development of(More)
This paper proposes regularised neural networks for characterisation of the multiple heterogeneous temporal dynamic patterns of gene expressions. Regularisation is developed to deal with noisy, high dimensional time course data and overfitting problems. We test the proposed model with a popular gene expression data. The model's performance is compared to(More)
Heterogeneous types of gene expressions may provide a better insight into the biological role of gene interaction with the environment, disease development and drug effect at the molecular level. In this paper for both exploring and prediction purposes a Time Lagged Recurrent Neural Network with trajectory learning is proposed for identifying and(More)