2014
Oliveira, S.; Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.
Predictive models for hospital bed management using data mining techniques Proceedings Article
Em: pp. 407-416, Springer Verlag, Madeira, 2014, ISSN: 21945357, (cited By 6; Conference of 2014 World Conference on Information Systems and Technologies, WorldCIST 2014 ; Conference Date: 15 April 2014 Through 18 April 2014; Conference Code:104563).
Resumo | Links | BibTeX | Etiquetas: Classification tasks; CRISP-DM; Cross industry; Hospital management; Patient discharge; Predictive models; Real environments; Resources management, Data mining; Decision trees; Forecasting; Hospital beds; Hospitals; Information systems; Patient monitoring; Support vector machines, Information management
@inproceedings{Oliveira2014407,
title = {Predictive models for hospital bed management using data mining techniques},
author = {S. Oliveira and F. Portela and M. F. Santos and J. Machado and A. Abelha},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898611818&doi=10.1007%2f978-3-319-05948-8_39&partnerID=40&md5=3faf01ae721a500d77e3cfe0b5a19b89},
doi = {10.1007/978-3-319-05948-8_39},
issn = {21945357},
year = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {276 VOLUME 2},
pages = {407-416},
publisher = {Springer Verlag},
address = {Madeira},
abstract = {It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. The use of Data Mining (DM) can contribute to overcome these limitations in order to identify relevant data on patient's management and providing important information for managers to support their decisions. Throughout this study, were induced DM models capable to make predictions in a real environment using real data. For this, was adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. Three distinct techniques were considered: Decision Trees (DT), Naïve Bayes (NB) and Support Vector Machine (SVM) to perform classification tasks. This work explored the possibility to predict the number of patient discharges using only the number of discharges veirifed in the past. The models developed are able to predict the number of patient discharges per week with acuity values ranging from ≈82.69% to ≈94.23%. The use of these models can improve the efficiency of the administration of hospital beds. An accurate forecasting of discharges allows a better estimate of the beds available for the coming weeks. © Springer International Publishing Switzerland 2014.},
note = {cited By 6; Conference of 2014 World Conference on Information Systems and Technologies, WorldCIST 2014 ; Conference Date: 15 April 2014 Through 18 April 2014; Conference Code:104563},
keywords = {Classification tasks; CRISP-DM; Cross industry; Hospital management; Patient discharge; Predictive models; Real environments; Resources management, Data mining; Decision trees; Forecasting; Hospital beds; Hospitals; Information systems; Patient monitoring; Support vector machines, Information management},
pubstate = {published},
tppubtype = {inproceedings}
}
It is clear that the failures found in hospital management are usually related to the lack of information and insufficient resources management. The use of Data Mining (DM) can contribute to overcome these limitations in order to identify relevant data on patient's management and providing important information for managers to support their decisions. Throughout this study, were induced DM models capable to make predictions in a real environment using real data. For this, was adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. Three distinct techniques were considered: Decision Trees (DT), Naïve Bayes (NB) and Support Vector Machine (SVM) to perform classification tasks. This work explored the possibility to predict the number of patient discharges using only the number of discharges veirifed in the past. The models developed are able to predict the number of patient discharges per week with acuity values ranging from ≈82.69% to ≈94.23%. The use of these models can improve the efficiency of the administration of hospital beds. An accurate forecasting of discharges allows a better estimate of the beds available for the coming weeks. © Springer International Publishing Switzerland 2014.