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Journal of Healthcare Engineering
Volume 2016 (2016), Article ID 7035463, 11 pages
Research Article

Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network

1Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
2Division of Health Insurance, Mackay Memorial Hospital, Taipei 10449, Taiwan
3Medical Affairs Department, Mackay Memorial Hospital, Taipei 10449, Taiwan
4Registration and Admitting, Mackay Memorial Hospital, Taipei 10449, Taiwan

Received 1 September 2015; Revised 21 February 2016; Accepted 15 March 2016

Academic Editor: Hélder A. Santos

Copyright © 2016 Pei-Fang (Jennifer) Tsai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.