Applying a BP neural network model to predict the length of hospital stay

Authors:
Jing-Song Li;Yu Tian;Yan-Feng Liu;Ting Shu;Ming-Hui Liang
Affiliations:
Heathcare Informatics Engineering Research Center, Zhejiang University, Hangzhou, China;Heathcare Informatics Engineering Research Center, Zhejiang University, Hangzhou, China;Heathcare Informatics Engineering Research Center, Zhejiang University, Hangzhou, China;National Institute of Hospital Administration, MOH of China, Beijing, China;National Institute of Hospital Administration, MOH of China, Beijing, China
Venue:
HIS'13 Proceedings of the second international conference on Health Information Science
Year:
2013

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Abstract

Length of hospital stay (LOS) is closely related to the control of medical costs and the management of hospital resources. In this study, we implemented a data mining approach based on Back-Propagation (BP) neural net-works to construct a LOS prediction model that can help doctors and nurses individualize patient treatment. We analyzed medical data from 921 patients whowere diagnosed as cholecystitis and treated in a Chinese hospital between 2003and 2007. Our prediction model achieved approximately 80% accuracy, and revealed 5 LOS predictors: days before operation, wound grade, operation approach, charge type and number of admissions. The model can be easily used toprovide suggestions for doctors and nurses determining patient LOS.