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http://dx.doi.org/10.14400/JDC.2018.16.1.231

Development of Predictive Model for Length of Stay(LOS) in Acute Stroke Patients using Artificial Intelligence  

Choi, Byung Kwan (Dept. of Neurosurgery, School of Medicine, Pusan National University)
Ham, Seung Woo (Korea Institute of Radiological & Medical Sciences)
Kim, Chok Hwan (Soonchunhyang University Cheonan Hospital)
Seo, Jung Sook (Severance Hospital, Yonsei University Health System)
Park, Myung Hwa (Korean Medical Record Association)
Kang, Sung-Hong (Dept. of Health Policy & Management, InJe University)
Publication Information
Journal of Digital Convergence / v.16, no.1, 2018 , pp. 231-242 More about this Journal
Abstract
The efficient management of the Length of Stay(LOS) is important in hospital. It is import to reduce medical cost for patients and increase profitability for hospitals. In order to efficiently manage LOS, it is necessary to develop an artificial intelligence-based prediction model that supports hospitals in benchmarking and reduction ways of LOS. In order to develop a predictive model of LOS for acute stroke patients, acute stroke patients were extracted from 2013 and 2014 discharge injury patient data. The data for analysis was classified as 60% for training and 40% for evaluation. In the model development, we used traditional regression technique such as multiple regression analysis method, artificial intelligence technique such as interactive decision tree, neural network technique, and ensemble technique which integrate all. Model evaluation used Root ASE (Absolute error) index. They were 23.7 by multiple regression, 23.7 by interactive decision tree, 22.7 by neural network and 22.7 by esemble technique. As a result of model evaluation, neural network technique which is artificial intelligence technique was found to be superior. Through this, the utility of artificial intelligence has been proved in the development of the prediction LOS model. In the future, it is necessary to continue research on how to utilize artificial intelligence techniques more effectively in the development of LOS prediction model.
Keywords
LOS prediction model; Artificial intelligence; Acute stroke; Neural network; Interactive decision tree;
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