WorldCIST'14 - The 2014 World Conference on Information Systems and Technologies

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Prediction Models for Hospital Bed Management using Data Mining techiques

It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. These aspects are crucial for the management of any organizational entity. The use of Data Mining (DM) can contribute to overcome this questions in order to identify relevant data on patient’s management and providing important information for managers to base their decisions.
Throughout this study, were induced DM models capable to make predictions in the hospital environment. The development of prediction models was conducted in a real environment with real data. For this, was adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. For this study three distinct techniques were used: Decision Tree (DT), Naïve Bayes (NB) and Support Vector Machine (SVM) to perform Classification tasks. The models developed are able to predict the number of patient discharges by week with an acuity values ranging from ≈82.69% and ≈94.23%.

Author(s):

Sérgio Oliveira    
University of Minho
Portugal

Filipe Portela    
Algoritmi Centre
Portugal

Manuel Filipe Santos    
Algoritmi Centre
Portugal

José Machado    
CCTC
Portugal

António Abelha    
CCTC
Portugal

 

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