Bayesian network analysis of accident risk in information-deficient scenarios
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Redalyc
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| Natura: | Artículo científico |
| Lingua: | en |
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Pontificia Universidad Católica de Chile
2017
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| _version_ | 1876487935146065920 |
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| author | José Enrique Martín |
| author_facet | José Enrique Martín |
| contents | Bayesian network analysis of accident risk in information-deficient scenarios José Enrique Martín Javier Taboada-García Saki Gerassis Ángeles Saavedra Roberto Martínez-Alegría Ingeniería model reduction Civil eng ineering workplace accident information deficit B ayesia n networks A nalysis of accidents using Bayesian networks links certain predictor factors with other target factors representing types of accidents under study . D atabases of real accident reports are typically used for both designing and training networks, which inevitably skews future inferences. In ferences are also limited because such databases do not usually include data on situations where accidents have not oc curred . Inferences can thus be made a bout the occurrence of an accident, but not about specific type s of accident. We describe a novel Bayesian network strategy for the fie ld of occupational risk prevention which , extracting data from a database that includes situations where no accident has occurred , quantifies the infl uence and interactions of factors. It also allows particular accident types to be studied individually, th ereby highlighting not only the correlation but also the causal relationship between work setting and accident. 2017 artículo científico 0717-7925 https://www.redalyc.org/articulo.oa?id=127654962008 en http://www.redalyc.org/revista.oa?id=1276 Revista de la Construcción application/pdf Pontificia Universidad Católica de Chile Revista de la Construcción (Chile) Num.3 Vol.16 |
| format | Artículo científico |
| id | redalyc_127654962008 |
| institution | Redalyc |
| language | en |
| publishDate | 2017 |
| publisher | Pontificia Universidad Católica de Chile |
| spellingShingle | Bayesian network analysis of accident risk in information-deficient scenarios José Enrique Martín Ingeniería model reduction Civil eng ineering workplace accident information deficit B ayesia n networks Bayesian network analysis of accident risk in information-deficient scenarios José Enrique Martín Javier Taboada-García Saki Gerassis Ángeles Saavedra Roberto Martínez-Alegría Ingeniería model reduction Civil eng ineering workplace accident information deficit B ayesia n networks A nalysis of accidents using Bayesian networks links certain predictor factors with other target factors representing types of accidents under study . D atabases of real accident reports are typically used for both designing and training networks, which inevitably skews future inferences. In ferences are also limited because such databases do not usually include data on situations where accidents have not oc curred . Inferences can thus be made a bout the occurrence of an accident, but not about specific type s of accident. We describe a novel Bayesian network strategy for the fie ld of occupational risk prevention which , extracting data from a database that includes situations where no accident has occurred , quantifies the infl uence and interactions of factors. It also allows particular accident types to be studied individually, th ereby highlighting not only the correlation but also the causal relationship between work setting and accident. 2017 artículo científico 0717-7925 https://www.redalyc.org/articulo.oa?id=127654962008 en http://www.redalyc.org/revista.oa?id=1276 Revista de la Construcción application/pdf Pontificia Universidad Católica de Chile Revista de la Construcción (Chile) Num.3 Vol.16 |
| title | Bayesian network analysis of accident risk in information-deficient scenarios |
| topic | Ingeniería model reduction Civil eng ineering workplace accident information deficit B ayesia n networks |
| url | https://www.redalyc.org/articulo.oa?id=127654962008 |