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- W2900381886 abstract "Nowadays, emergency departments are confronted to exceptional events such as epidemics problems or health crises. These situations increase the patient flow. The consequence of this influx of patients has resulted in problems of ED overcrowding which often increases the length of stay of patients (LOS) in EDs and leads to strain situations. To cope with such situations, ED managers must predict the LOS. In this paper, we propose a model for predicting the patient length of stay (LOS) in ED using data mining techniques. The used data was collected from the pediatric emergency department (PED) in Lille regional hospital centre, France. Our target is to illustrate with a real world case study, how data mining can be benefit to predict LOS and which its limitations." @default.
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- W2900381886 date "2014-11-05" @default.
- W2900381886 modified "2023-09-26" @default.
- W2900381886 title "A DATA MINING-BASED APPROACH TO PREDICT STRAIN SITUATIONS IN HOSPITAL EMERGENCY DEPARTMENT SYSTEMS" @default.
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