2013
Portela, F.; Santos, M. F.; Machado, J.; Abelha, A.; Silva, Á.
Pervasive and intelligent decision support in critical health care using ensembles Proceedings Article
In: pp. 1-16, Prague, 2013, ISSN: 03029743, (cited By 27; Conference of 4th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2013 ; Conference Date: 28 August 2013 Through 28 August 2013; Conference Code:99901).
Abstract | Links | BibTeX | Etiquetas: Artificial intelligence; Automobile drivers; Decision support systems; Forecasting; Health care; Information science; Intensive care units, Data mining models; Decision making process; Ensemble approaches; Ensemble strategies; Intelligent decision support; Intelligent decision support systems; Organ failure; Real-time data processing, Information technology
@inproceedings{Portela20131,
title = {Pervasive and intelligent decision support in critical health care using ensembles},
author = {F. Portela and M. F. Santos and J. Machado and A. Abelha and Á. Silva},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84885207111&doi=10.1007%2f978-3-642-40093-3_1&partnerID=40&md5=5efa035b80b06da0c05d28fe4cbb2f44},
doi = {10.1007/978-3-642-40093-3_1},
issn = {03029743},
year = {2013},
date = {2013-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {8060 LNCS},
pages = {1-16},
address = {Prague},
abstract = {Critical health care is one of the most difficult areas to make decisions. Every day new situations appear and doctors need to decide very quickly. Moreover, it is difficult to have an exact perception of the patient situation and a precise prediction on the future condition. The introduction of Intelligent Decision Support Systems (IDSS) in this area can help the doctors in the decision making process, giving them an important support based in new knowledge. Previous work has demonstrated that is possible to use data mining models to predict future situations of patients. Even so, two other problems arise: i) how fast; and ii) how accurate? To answer these questions, an ensemble strategy was experimented in the context of INTCare system, a pervasive IDSS to automatically predict the organ failure and the outcome of the patients throughout next 24 hours. This paper presents the results obtained combining real-time data processing with ensemble approach in the intensive care unit of the Centro Hospitalar do Porto, Porto, Portugal. © 2013 Springer-Verlag.},
note = {cited By 27; Conference of 4th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2013 ; Conference Date: 28 August 2013 Through 28 August 2013; Conference Code:99901},
keywords = {Artificial intelligence; Automobile drivers; Decision support systems; Forecasting; Health care; Information science; Intensive care units, Data mining models; Decision making process; Ensemble approaches; Ensemble strategies; Intelligent decision support; Intelligent decision support systems; Organ failure; Real-time data processing, Information technology},
pubstate = {published},
tppubtype = {inproceedings}
}
Critical health care is one of the most difficult areas to make decisions. Every day new situations appear and doctors need to decide very quickly. Moreover, it is difficult to have an exact perception of the patient situation and a precise prediction on the future condition. The introduction of Intelligent Decision Support Systems (IDSS) in this area can help the doctors in the decision making process, giving them an important support based in new knowledge. Previous work has demonstrated that is possible to use data mining models to predict future situations of patients. Even so, two other problems arise: i) how fast; and ii) how accurate? To answer these questions, an ensemble strategy was experimented in the context of INTCare system, a pervasive IDSS to automatically predict the organ failure and the outcome of the patients throughout next 24 hours. This paper presents the results obtained combining real-time data processing with ensemble approach in the intensive care unit of the Centro Hospitalar do Porto, Porto, Portugal. © 2013 Springer-Verlag.