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.
journal_name
J Healthc Engjournal_title
Journal of healthcare engineeringauthors
Tsai PF,Chen PC,Chen YY,Song HY,Lin HM,Lin FM,Huang QPdoi
10.1155/2016/7035463subject
Has Abstractpub_date
2016-01-01 00:00:00eissn
2040-2295issn
2040-2309pii
7035463journal_volume
2016pub_type
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