2013
Santos, M. F.; Portela, F.; Miranda, M.; Machado, J.; Abelha, A.; Silva, A.
Grid data mining strategies for outcome prediction in distributed intensive care units Book Chapter
Em: pp. 87-101, IGI Global, 2013, ISBN: 9781466636682; 146663667X; 9781466636675, (cited By 5).
Resumo | Links | BibTeX | Etiquetas: Data mining, Distributed data mining; Distributed data sources; Experimental test; Grid computing environment; Intensive care medicines; Learning classifier system; Local prediction; Outcome prediction, Forecasting; Grid computing; Intensive care units; Medical applications; Statistical tests
@inbook{Santos201387,
title = {Grid data mining strategies for outcome prediction in distributed intensive care units},
author = {M. F. Santos and F. Portela and M. Miranda and J. Machado and A. Abelha and A. Silva},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84898257202&doi=10.4018%2f978-1-4666-3667-5.ch006&partnerID=40&md5=7a3d1bc440644ae630ef241a97c230a6},
doi = {10.4018/978-1-4666-3667-5.ch006},
isbn = {9781466636682; 146663667X; 9781466636675},
year = {2013},
date = {2013-01-01},
journal = {Information Systems and Technologies for Enhancing Health and Social Care},
pages = {87-101},
publisher = {IGI Global},
abstract = {Previous work developed to predict the outcome of patients in the context of intensive care units brought to the light some requirements like the need to deal with distributed data sources. Those data sources can be used to induce local prediction models, and those models can in turn be used to induce global models more accurate and more general than the local models. This chapter introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Five different tactics are explored for constructing the global model in a Distributed Data Mining (DDM) approach: Generalized Classifier Method (GCM), Specific Classifier Method (SCM), Weighed Classifier Method (WCM), Majority Voting Method (MVM), and Model Sampling Method (MSM). Experimental tests were conducted with a real world data set from intensive care medicine. The results demonstrate that the performance of DDM methods is very competitive when compared with the centralized methods. © 2013, IGI Global.},
note = {cited By 5},
keywords = {Data mining, Distributed data mining; Distributed data sources; Experimental test; Grid computing environment; Intensive care medicines; Learning classifier system; Local prediction; Outcome prediction, Forecasting; Grid computing; Intensive care units; Medical applications; Statistical tests},
pubstate = {published},
tppubtype = {inbook}
}
Previous work developed to predict the outcome of patients in the context of intensive care units brought to the light some requirements like the need to deal with distributed data sources. Those data sources can be used to induce local prediction models, and those models can in turn be used to induce global models more accurate and more general than the local models. This chapter introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. Five different tactics are explored for constructing the global model in a Distributed Data Mining (DDM) approach: Generalized Classifier Method (GCM), Specific Classifier Method (SCM), Weighed Classifier Method (WCM), Majority Voting Method (MVM), and Model Sampling Method (MSM). Experimental tests were conducted with a real world data set from intensive care medicine. The results demonstrate that the performance of DDM methods is very competitive when compared with the centralized methods. © 2013, IGI Global.