Grid data mining strategies for outcome prediction in distributed intensive care units

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 induc...

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Detalhes bibliográficos
Autor principal: Santos, Manuel Filipe (author)
Outros Autores: Portela, Filipe (author), Miranda, Miguel (author), Machado, José Manuel (author), Abelha, António (author), Silva, Álvaro (author), Rua, Fernando (author)
Formato: bookPart
Idioma:eng
Publicado em: 2013
Assuntos:
Intensive care medicine
Outcome prediction
Distributed data mining
Grid computing
Centralized data mining
Texto completo:http://hdl.handle.net/1822/21716
País:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/21716
Descrição
Resumo: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 paper 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 the intensive care medicine. The results demonstrate that the performance of DDM methods is very competitive when compared with the centralized methods.

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