2015
Silva, E.; Cardoso, L.; Portela, F.; Abelha, A.; Santos, M. F.; Machado, J.
Predicting nosocomial infection by using data mining technologies Proceedings Article
Em: A., Reis L. P. Rocha A. Rocha (Ed.): pp. 189-198, Springer Verlag, 2015, ISSN: 21945357, (cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Cost reduction; Data mining; Health care; Information systems; Patient treatment, Classification technique; Clinical decision support systems; CRISP-DM; Data mining technology; Healthcare environments; Healthcare institutions; Knowledge discovery in database; Nosocomial infection, Decision support systems
@inproceedings{Silva2015189,
title = {Predicting nosocomial infection by using data mining technologies},
author = {E. Silva and L. Cardoso and F. Portela and A. Abelha and M. F. Santos and J. Machado},
editor = {Reis L. P. Rocha A. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925325130&doi=10.1007%2f978-3-319-16528-8_18&partnerID=40&md5=043beaeb8726c7a044de6bc1cc09a0ef},
doi = {10.1007/978-3-319-16528-8_18},
issn = {21945357},
year = {2015},
date = {2015-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {354},
pages = {189-198},
publisher = {Springer Verlag},
abstract = {The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naïve Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being. © Springer International Publishing Switzerland 2015.},
note = {cited By 11; Conference of World Conference on Information Systems and Technologies, WorldCIST 2015 ; Conference Date: 1 April 2015 Through 3 April 2015; Conference Code:115559},
keywords = {Artificial intelligence; Cost reduction; Data mining; Health care; Information systems; Patient treatment, Classification technique; Clinical decision support systems; CRISP-DM; Data mining technology; Healthcare environments; Healthcare institutions; Knowledge discovery in database; Nosocomial infection, Decision support systems},
pubstate = {published},
tppubtype = {inproceedings}
}
The existence of nosocomial infection prevision systems in healthcare environments can contribute to improve the quality of the healthcare institution and also to reduce the costs with the treatment of the patients that acquire these infections. The analysis of the information available allows to efficiently prevent these infections and to build knowledge that can help to identify their eventual occurrence. This paper presents the results of the application of predictive models to real clinical data. Good models, induced by the Data Mining (DM) classification techniques Support Vector Machines and Naïve Bayes, were achieved (sensitivities higher than 91.90%). Therefore, with these models that be able to predict these infections may allow the prevention and, consequently, the reduction of nosocomial infection incidence. They should act as a Clinical Decision Support System (CDSS) capable of reducing nosocomial infections and the associated costs, improving the healthcare and, increasing patients’ safety and well-being. © Springer International Publishing Switzerland 2015.