Literature DB >> 15081073

A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service.

Silvia Acid1, Luis M de Campos, Juan M Fernández-Luna, Susana Rodríguez, José María Rodríguez, José Luis Salcedo.   

Abstract

Due to the uncertainty of many of the factors that influence the performance of an emergency medical service, we propose using Bayesian networks to model this kind of system. We use different algorithms for learning Bayesian networks in order to build several models, from the hospital manager's point of view, and apply them to the specific case of the emergency service of a Spanish hospital. This first study of a real problem includes preliminary data processing, the experiments carried out, the comparison of the algorithms from different perspectives, and some potential uses of Bayesian networks for management problems in the health service.

Mesh:

Year:  2004        PMID: 15081073     DOI: 10.1016/j.artmed.2003.11.002

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  4 in total

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Authors:  Emily W Watt; Emily Watt; Alex A T Bui; Alex At Bui
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

2.  Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy.

Authors:  Wei Liu; Wen Zhu; Bo Liao; Xiangtao Chen
Journal:  PLoS One       Date:  2016-11-09       Impact factor: 3.240

3.  Learning partially directed functional networks from meta-analysis imaging data.

Authors:  Jane Neumann; Peter T Fox; Robert Turner; Gabriele Lohmann
Journal:  Neuroimage       Date:  2009-10-06       Impact factor: 6.556

4.  Seeded Bayesian Networks: constructing genetic networks from microarray data.

Authors:  Amira Djebbari; John Quackenbush
Journal:  BMC Syst Biol       Date:  2008-07-04
  4 in total

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