Using classification and regression trees for a nurse activity simulation

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

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

1 Citation (Scopus)

Abstract

This research develops a novel approach for constructing simulation models based on a real data set provided by Baylor Regional Medical Center (Baylor) in Grapevine, Texas. Kernel density estimates and trees were utilized to extract important knowledge about the workload of nurses from an encrypted data set provided by Baylor for four care units. The four units include two medical/surgical units, one mom/baby unit, and one high-risk labor-and-delivery unit. Classification and Regression Trees, a data mining tool for prediction and classification, was applied to the Baylor data 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. Application of this approach for simulation-based nurse-patient assignments is discussed.

Original languageEnglish
Title of host publication2006 IIE Annual Conference and Exhibition
Publication statusPublished - 2006 Dec 1
Externally publishedYes
Event2006 IIE Annual Conference and Exposition - Orlando, FL, United States
Duration: 2006 May 202006 May 24

Other

Other2006 IIE Annual Conference and Exposition
CountryUnited States
CityOrlando, FL
Period06/5/2006/5/24

Fingerprint

Data mining
Personnel

Keywords

  • Classification tree
  • Nurse assignment
  • Simulation

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Sundaramoorthi, D., Chen, V. C. P., Rosenberger, J. M., Kim, S. B., & Buckley-Behan, D. F. (2006). Using classification and regression trees for a nurse activity simulation. In 2006 IIE Annual Conference and Exhibition

Using classification and regression trees for a nurse activity simulation. / Sundaramoorthi, Durai; Chen, Victoria C P; Rosenberger, Jay M.; Kim, Seoung Bum; Buckley-Behan, Deborah F.

2006 IIE Annual Conference and Exhibition. 2006.

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

Sundaramoorthi, D, Chen, VCP, Rosenberger, JM, Kim, SB & Buckley-Behan, DF 2006, Using classification and regression trees for a nurse activity simulation. in 2006 IIE Annual Conference and Exhibition. 2006 IIE Annual Conference and Exposition, Orlando, FL, United States, 06/5/20.
Sundaramoorthi D, Chen VCP, Rosenberger JM, Kim SB, Buckley-Behan DF. Using classification and regression trees for a nurse activity simulation. In 2006 IIE Annual Conference and Exhibition. 2006
Sundaramoorthi, Durai ; Chen, Victoria C P ; Rosenberger, Jay M. ; Kim, Seoung Bum ; Buckley-Behan, Deborah F. / Using classification and regression trees for a nurse activity simulation. 2006 IIE Annual Conference and Exhibition. 2006.
@inproceedings{dfeb17361b034906844f8150b3eb3b8c,
title = "Using classification and regression trees for a nurse activity simulation",
abstract = "This research develops a novel approach for constructing simulation models based on a real data set provided by Baylor Regional Medical Center (Baylor) in Grapevine, Texas. Kernel density estimates and trees were utilized to extract important knowledge about the workload of nurses from an encrypted data set provided by Baylor for four care units. The four units include two medical/surgical units, one mom/baby unit, and one high-risk labor-and-delivery unit. Classification and Regression Trees, a data mining tool for prediction and classification, was applied to the Baylor data 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. Application of this approach for simulation-based nurse-patient assignments is discussed.",
keywords = "Classification tree, Nurse assignment, Simulation",
author = "Durai Sundaramoorthi and Chen, {Victoria C P} and Rosenberger, {Jay M.} and Kim, {Seoung Bum} and Buckley-Behan, {Deborah F.}",
year = "2006",
month = "12",
day = "1",
language = "English",
booktitle = "2006 IIE Annual Conference and Exhibition",

}

TY - GEN

T1 - Using classification and regression trees for a nurse activity simulation

AU - Sundaramoorthi, Durai

AU - Chen, Victoria C P

AU - Rosenberger, Jay M.

AU - Kim, Seoung Bum

AU - Buckley-Behan, Deborah F.

PY - 2006/12/1

Y1 - 2006/12/1

N2 - This research develops a novel approach for constructing simulation models based on a real data set provided by Baylor Regional Medical Center (Baylor) in Grapevine, Texas. Kernel density estimates and trees were utilized to extract important knowledge about the workload of nurses from an encrypted data set provided by Baylor for four care units. The four units include two medical/surgical units, one mom/baby unit, and one high-risk labor-and-delivery unit. Classification and Regression Trees, a data mining tool for prediction and classification, was applied to the Baylor data 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. Application of this approach for simulation-based nurse-patient assignments is discussed.

AB - This research develops a novel approach for constructing simulation models based on a real data set provided by Baylor Regional Medical Center (Baylor) in Grapevine, Texas. Kernel density estimates and trees were utilized to extract important knowledge about the workload of nurses from an encrypted data set provided by Baylor for four care units. The four units include two medical/surgical units, one mom/baby unit, and one high-risk labor-and-delivery unit. Classification and Regression Trees, a data mining tool for prediction and classification, was applied to the Baylor data 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. Application of this approach for simulation-based nurse-patient assignments is discussed.

KW - Classification tree

KW - Nurse assignment

KW - Simulation

UR - http://www.scopus.com/inward/record.url?scp=36448930571&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=36448930571&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:36448930571

BT - 2006 IIE Annual Conference and Exhibition

ER -