Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network. (7th April 2016)

• Record Type: Journal Article
• Authors: Tsai, Pei-Fang (Jennifer); Chen, Po-Chia; Chen, Yen-You; Song, Hao-Yuan; Lin, Hsiu-Mei; Lin, Fu-Man; Huang, Qiou-Pieng
• Other Names: Santos Hélder A. Academic Editor.
• Is Part Of: Journal of healthcare engineering. Volume 2016(2016)
• Publication Date: 2016-04-07
• Subjects: Hospital buildings -- Environmental engineering -- Periodicals; Medical technology -- Periodicals; Medical informatics -- Periodicals; 610.28
• DOI: 10.1155/2016/7035463
• Languages: English
• ISSNs: 2040-2295

Abstract

Abstract : 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.