Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network
Joint Authors
Tsai, Pei-Fang (Jennifer)
Chen, Po-Chia
Chen, Yen-You
Song, Hao-Yuan
Lin, Hsiu-Mei
Lin, Fu-Man
Huang, Qiou-Pieng
Source
Journal of Healthcare Engineering
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-04-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
For hospitals’ admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care.
This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan.
A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed.
Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage.
For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.
American Psychological Association (APA)
Tsai, Pei-Fang (Jennifer)& Chen, Po-Chia& Chen, Yen-You& Song, Hao-Yuan& Lin, Hsiu-Mei& Lin, Fu-Man…[et al.]. 2016. Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network. Journal of Healthcare Engineering،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1108693
Modern Language Association (MLA)
Tsai, Pei-Fang (Jennifer)…[et al.]. Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network. Journal of Healthcare Engineering No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1108693
American Medical Association (AMA)
Tsai, Pei-Fang (Jennifer)& Chen, Po-Chia& Chen, Yen-You& Song, Hao-Yuan& Lin, Hsiu-Mei& Lin, Fu-Man…[et al.]. Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network. Journal of Healthcare Engineering. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1108693
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1108693