A data-integrated nurse activity simulation model
Abstract
This research develops a data-integrated approach for constructing simulation models based on a real data set provided by Baylor Regional Medical Center (Baylor) in Grapevine, Texas. Tree-based models and kernel density estimation were utilized to extract important knowledge from the data for the simulation. Classification and Regression Tree model, a data mining tool for prediction and classification, was used to develop two tree structures: (a) 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; and (b) a classification tree, from which transition probabilities for nurse movements are determined. Kernel density estimation is used to estimate the continuous distribution for the amount of time a nurse spends in a location. Merits of using our approach for Baylor's nurse activity simulation are discussed.
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Published In
December 2006
2429 pages
ISBN:1424405017
- Editors:
- L. Felipe Perrone,
- Barry G. Lawson,
- Jason Liu,
- Frederick P. Wieland,
- General Chair:
- David Nicol,
- Program Chair:
- Richard Fujimoto
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- IIE: Institute of Industrial Engineers
- ASA: American Statistical Association
- IEICE ESS: Institute of Electronics, Information and Communication Engineers, Engineering Sciences Society
- IEEE-CS\DATC: The IEEE Computer Society
- SIGSIM: ACM Special Interest Group on Simulation and Modeling
- NIST: National Institute of Standards and Technology
- (SCS): The Society for Modeling and Simulation International
- INFORMS-CS: Institute for Operations Research and the Management Sciences-College on Simulation
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Winter Simulation Conference
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Published: 03 December 2006
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- IEICE ESS
- IEEE-CS\DATC
- SIGSIM
- NIST
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- INFORMS-CS
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WSC '06 Paper Acceptance Rate 177 of 252 submissions, 70%;
Overall Acceptance Rate 3,413 of 5,075 submissions, 67%
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