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Citations
... Regression methods are one of the traditional tools used for prediction (Neter et al., 1996; Hastie et al., 2001; Walpole et al., 2002). Multivariate Adaptive Regression Splines (MARS), a spline based prediction model (Friedman, 1991) was recently applied to different prediction problems (Chen et al., 1999; Tsai et al., 2003; Chen et al., 2003; Siddappa et al., 2006; Pilla et al., 2005). Neural networks, a nonlinear statistical model (Ripley, 1996; Haykin, 1999), often represented by a network diagram, can be used for prediction or classification. ...
This research develops a novel data-integrated simulation to evaluate nurse-patient assignments (SIMNA) based on a real data set provided by a northeast Texas hospital. Tree-based models and kernel density estimation (KDE) were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree models, data mining tools for prediction and classification, were used to develop five tree structures: (a) four classification trees from which transition probabilities for nurse movements are determined, and (b) a regression tree from which the amount of time a nurse spends in a location is predicted based on factors such as the primary diagnosis of a patient and the type of nurse. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Results obtained from SIMNA to evaluate nurse-patient assignments in Medical/Surgical unit I of the northeast Texas hospital are discussed.
