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 language | English |
|---|---|
| Title of host publication | 2006 IIE Annual Conference and Exhibition |
| Publication status | Published - 2006 Dec 1 |
| Externally published | Yes |
| Event | 2006 IIE Annual Conference and Exposition - Orlando, FL, United States Duration: 2006 May 20 → 2006 May 24 |
Other
| Other | 2006 IIE Annual Conference and Exposition |
|---|---|
| Country | United States |
| City | Orlando, FL |
| Period | 06/5/20 → 06/5/24 |
Fingerprint
Keywords
- Classification tree
- Nurse assignment
- Simulation
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
Cite this
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 proceeding › Conference contribution
}
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 -