A data-integrated nurse activity simulation model

Durai Sundaramoorthi, Victoria C P Chen, Seoung Bum Kim, Jay M. Rosenberger, Deborah F. Buckley-Behan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationProceedings - Winter Simulation Conference
Pages960-966
Number of pages7
DOIs
Publication statusPublished - 2006 Dec 1
Externally publishedYes
Event2006 Winter Simulation Conference, WSC - Monterey, CA, United States
Duration: 2006 Dec 32006 Dec 6

Other

Other2006 Winter Simulation Conference, WSC
CountryUnited States
CityMonterey, CA
Period06/12/306/12/6

Fingerprint

Data mining

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Sundaramoorthi, D., Chen, V. C. P., Kim, S. B., Rosenberger, J. M., & Buckley-Behan, D. F. (2006). A data-integrated nurse activity simulation model. In Proceedings - Winter Simulation Conference (pp. 960-966). [4117706] https://doi.org/10.1109/WSC.2006.323182

A data-integrated nurse activity simulation model. / Sundaramoorthi, Durai; Chen, Victoria C P; Kim, Seoung Bum; Rosenberger, Jay M.; Buckley-Behan, Deborah F.

Proceedings - Winter Simulation Conference. 2006. p. 960-966 4117706.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sundaramoorthi, D, Chen, VCP, Kim, SB, Rosenberger, JM & Buckley-Behan, DF 2006, A data-integrated nurse activity simulation model. in Proceedings - Winter Simulation Conference., 4117706, pp. 960-966, 2006 Winter Simulation Conference, WSC, Monterey, CA, United States, 06/12/3. https://doi.org/10.1109/WSC.2006.323182
Sundaramoorthi D, Chen VCP, Kim SB, Rosenberger JM, Buckley-Behan DF. A data-integrated nurse activity simulation model. In Proceedings - Winter Simulation Conference. 2006. p. 960-966. 4117706 https://doi.org/10.1109/WSC.2006.323182
Sundaramoorthi, Durai ; Chen, Victoria C P ; Kim, Seoung Bum ; Rosenberger, Jay M. ; Buckley-Behan, Deborah F. / A data-integrated nurse activity simulation model. Proceedings - Winter Simulation Conference. 2006. pp. 960-966
@inproceedings{c03548bc401e48a1b430c30c8a35a1d2,
title = "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.",
author = "Durai Sundaramoorthi and Chen, {Victoria C P} and Kim, {Seoung Bum} and Rosenberger, {Jay M.} and Buckley-Behan, {Deborah F.}",
year = "2006",
month = "12",
day = "1",
doi = "10.1109/WSC.2006.323182",
language = "English",
isbn = "1424405017",
pages = "960--966",
booktitle = "Proceedings - Winter Simulation Conference",

}

TY - GEN

T1 - A data-integrated nurse activity simulation model

AU - Sundaramoorthi, Durai

AU - Chen, Victoria C P

AU - Kim, Seoung Bum

AU - Rosenberger, Jay M.

AU - Buckley-Behan, Deborah F.

PY - 2006/12/1

Y1 - 2006/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=46149113961&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=46149113961&partnerID=8YFLogxK

U2 - 10.1109/WSC.2006.323182

DO - 10.1109/WSC.2006.323182

M3 - Conference contribution

SN - 1424405017

SN - 9781424405015

SP - 960

EP - 966

BT - Proceedings - Winter Simulation Conference

ER -