2013
Portela, F.; Santos, M. F.; Silva, Á.; Machado, J.; Abelha, A.; Rua, F.
Data mining for real-time Intelligent Decision Support System in intensive care medicine Proceedings Article
Em: pp. 270-276, Barcelona, 2013, ISBN: 9789898565389, (cited By 6; Conference of 5th International Conference on Agents and Artificial Intelligence, ICAART 2013 ; Conference Date: 15 February 2013 Through 18 February 2013; Conference Code:97005).
Resumo | Links | BibTeX | Etiquetas: Artificial intelligence; Automobile drivers; Data mining; Decision making; Decision support systems; Decision trees, Data engineering; Data mining models; Decision making process; Intelligent decision support systems; Intensive care medicines; Predictive models; Real time; Real-time Intelligent Decision Support Systems, Intensive care units
@inproceedings{Portela2013270,
title = {Data mining for real-time Intelligent Decision Support System in intensive care medicine},
author = {F. Portela and M. F. Santos and Á. Silva and J. Machado and A. Abelha and F. Rua},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84877954083&partnerID=40&md5=2fe4f3dbeb72a99c6e8cfa657dc52383},
isbn = {9789898565389},
year = {2013},
date = {2013-01-01},
journal = {ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence},
volume = {2},
pages = {270-276},
address = {Barcelona},
abstract = {The introduction of Intelligent Decision Support Systems (IDSS) in critical areas like Intensive Medicine is a complex and difficult process. The professionals of Intensive Care Units (ICU) haven't much time to register data because the direct care to the patients is always mandatory. In order to help doctors in the decision making process, the INTCare system has been deployed in the ICU of Centro Hospitalar of Porto, Portugal. INTCare is an IDSS that makes use of data mining models to predict the outcome and the organ failure probability for the ICU patients. This paper introduces the work carried out in order to automate the processes of data acquisition and data mining. The main goal of this work is to reduce significantly the manual efforts of the staff in the ICU. All the processes are autonomous and are executed in real-time. In particular, Decision Trees, Support Vector Machines and Naïve Bayes were used with online data to continuously adapt the predictive models. The data engineering process and achieved results, in terms of the performance attained, will be presented.},
note = {cited By 6; Conference of 5th International Conference on Agents and Artificial Intelligence, ICAART 2013 ; Conference Date: 15 February 2013 Through 18 February 2013; Conference Code:97005},
keywords = {Artificial intelligence; Automobile drivers; Data mining; Decision making; Decision support systems; Decision trees, Data engineering; Data mining models; Decision making process; Intelligent decision support systems; Intensive care medicines; Predictive models; Real time; Real-time Intelligent Decision Support Systems, Intensive care units},
pubstate = {published},
tppubtype = {inproceedings}
}
The introduction of Intelligent Decision Support Systems (IDSS) in critical areas like Intensive Medicine is a complex and difficult process. The professionals of Intensive Care Units (ICU) haven't much time to register data because the direct care to the patients is always mandatory. In order to help doctors in the decision making process, the INTCare system has been deployed in the ICU of Centro Hospitalar of Porto, Portugal. INTCare is an IDSS that makes use of data mining models to predict the outcome and the organ failure probability for the ICU patients. This paper introduces the work carried out in order to automate the processes of data acquisition and data mining. The main goal of this work is to reduce significantly the manual efforts of the staff in the ICU. All the processes are autonomous and are executed in real-time. In particular, Decision Trees, Support Vector Machines and Naïve Bayes were used with online data to continuously adapt the predictive models. The data engineering process and achieved results, in terms of the performance attained, will be presented.