2013
Portela, F.; Santos, M. F.; Silva, Á.; Abelha, A.; Machado, J.
Pervasive Ensemble Data Mining Models to Predict Organ Failure and Patient Outcome in Intensive Medicine Proceedings Article
Em: pp. 410-425, Springer Verlag, Barcelona, 2013, ISSN: 18650929, (cited By 1; Conference of 4th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2012 ; Conference Date: 4 October 2012 Through 7 October 2012; Conference Code:106350).
Resumo | Links | BibTeX | Etiquetas: Data mining, Decision support systems; Intensive care units; Knowledge engineering; Knowledge management, Ensemble; INTCare; Intensive care; Organ failure; Patient Outcome; Pervasive healthcare; Real-time
@inproceedings{Portela2013410,
title = {Pervasive Ensemble Data Mining Models to Predict Organ Failure and Patient Outcome in Intensive Medicine},
author = {F. Portela and M. F. Santos and Á. Silva and A. Abelha and J. Machado},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904295892&doi=10.1007%2f978-3-642-54105-6_27&partnerID=40&md5=b95b894d9b73bbb6bdc192f7fe27359f},
doi = {10.1007/978-3-642-54105-6_27},
issn = {18650929},
year = {2013},
date = {2013-01-01},
journal = {Communications in Computer and Information Science},
volume = {415},
pages = {410-425},
publisher = {Springer Verlag},
address = {Barcelona},
abstract = {The number of patients admitted to Intensive Care Units with organ failure is significant. This type of situation is very common in Intensive Medicine. Intensive medicine is a specific area of medicine whose purpose is to avoid organ failure and recover patients in weak conditions. This type of problems can culminate in the death of patient. In order to help the intensive medicine professionals at the exact moment of decision making, a Pervasive Intelligent Decision Support System called INTCare was developed. INTCare uses ensemble data mining to predict the probability of occurring an organ failure or patient death for the next hour. To assure the better results, a measure was implemented to assess the models quality. The transforming process and model induction are both performed automatically and in real-time. The ensemble uses online-learning to improve the models. This paper explores the ensemble approach to improve the decision process in intensive Medicine. © Springer-Verlag Berlin Heidelberg 2013.},
note = {cited By 1; Conference of 4th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2012 ; Conference Date: 4 October 2012 Through 7 October 2012; Conference Code:106350},
keywords = {Data mining, Decision support systems; Intensive care units; Knowledge engineering; Knowledge management, Ensemble; INTCare; Intensive care; Organ failure; Patient Outcome; Pervasive healthcare; Real-time},
pubstate = {published},
tppubtype = {inproceedings}
}
The number of patients admitted to Intensive Care Units with organ failure is significant. This type of situation is very common in Intensive Medicine. Intensive medicine is a specific area of medicine whose purpose is to avoid organ failure and recover patients in weak conditions. This type of problems can culminate in the death of patient. In order to help the intensive medicine professionals at the exact moment of decision making, a Pervasive Intelligent Decision Support System called INTCare was developed. INTCare uses ensemble data mining to predict the probability of occurring an organ failure or patient death for the next hour. To assure the better results, a measure was implemented to assess the models quality. The transforming process and model induction are both performed automatically and in real-time. The ensemble uses online-learning to improve the models. This paper explores the ensemble approach to improve the decision process in intensive Medicine. © Springer-Verlag Berlin Heidelberg 2013.