@article{
 id = {c1eb884f-7ad9-34f6-abbb-208dfee0e704},
 title = {Prognostic Bayesian networks II: an application in the domain of cardiac surgery.},
 type = {article},
 year = {2007},
 created = {2015-04-11T19:52:21.000Z},
 pages = {619-30},
 volume = {40},
 websites = {http://www.sciencedirect.com/science/article/pii/S1532046407000639},
 month = {12},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},
 last_modified = {2015-04-11T19:52:21.000Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 abstract = {A prognostic Bayesian network (PBN) is new type of prognostic model that implements a dynamic, process-oriented view on prognosis. In a companion article, the rationale of the PBN is described, and a dedicated learning procedure is presented. This article presents an application here of in the domain of cardiac surgery. A PBN is induced from clinical data of cardiac surgical patients using the proposed learning procedure; hospital mortality is used as outcome variable. The predictive performance of the PBN is evaluated on an independent test set, and results were compared to the performance of a network that was induced using a standard algorithm where candidate networks are selected using the minimal description length principle. The PBN is embedded in the prognostic system ProCarSur; a prototype of this system is presented. This application shows PBNs as a useful prognostic tool in medical processes. In addition, the article shows the added value of the PBN learning procedure.},
 bibtype = {article},
 author = {},
 journal = {Journal of biomedical informatics},
 number = {6}
}

{"_id":"YZKcvMurP9AyRkzrj","authorIDs":[],"author_short":[],"bibbaseid":"prognosticbayesiannetworksiianapplicationinthedomainofcardiacsurgery-2007","bibdata":{"id":"c1eb884f-7ad9-34f6-abbb-208dfee0e704","title":"Prognostic Bayesian networks II: an application in the domain of cardiac surgery.","type":"article","year":"2007","created":"2015-04-11T19:52:21.000Z","pages":"619-30","volume":"40","websites":"http://www.sciencedirect.com/science/article/pii/S1532046407000639","month":"12","accessed":"2015-04-11","file_attached":false,"profile_id":"95e10851-cdf3-31de-9f82-1ab629e601b0","group_id":"db853ba9-040a-35e0-8f4f-fe9d1f87c7b5","last_modified":"2015-04-11T19:52:21.000Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"abstract":"A prognostic Bayesian network (PBN) is new type of prognostic model that implements a dynamic, process-oriented view on prognosis. In a companion article, the rationale of the PBN is described, and a dedicated learning procedure is presented. This article presents an application here of in the domain of cardiac surgery. A PBN is induced from clinical data of cardiac surgical patients using the proposed learning procedure; hospital mortality is used as outcome variable. The predictive performance of the PBN is evaluated on an independent test set, and results were compared to the performance of a network that was induced using a standard algorithm where candidate networks are selected using the minimal description length principle. The PBN is embedded in the prognostic system ProCarSur; a prototype of this system is presented. This application shows PBNs as a useful prognostic tool in medical processes. In addition, the article shows the added value of the PBN learning procedure.","bibtype":"article","author":[],"journal":"Journal of biomedical informatics","number":"6","bibtex":"@article{\n id = {c1eb884f-7ad9-34f6-abbb-208dfee0e704},\n title = {Prognostic Bayesian networks II: an application in the domain of cardiac surgery.},\n type = {article},\n year = {2007},\n created = {2015-04-11T19:52:21.000Z},\n pages = {619-30},\n volume = {40},\n websites = {http://www.sciencedirect.com/science/article/pii/S1532046407000639},\n month = {12},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {db853ba9-040a-35e0-8f4f-fe9d1f87c7b5},\n last_modified = {2015-04-11T19:52:21.000Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n abstract = {A prognostic Bayesian network (PBN) is new type of prognostic model that implements a dynamic, process-oriented view on prognosis. In a companion article, the rationale of the PBN is described, and a dedicated learning procedure is presented. This article presents an application here of in the domain of cardiac surgery. A PBN is induced from clinical data of cardiac surgical patients using the proposed learning procedure; hospital mortality is used as outcome variable. The predictive performance of the PBN is evaluated on an independent test set, and results were compared to the performance of a network that was induced using a standard algorithm where candidate networks are selected using the minimal description length principle. The PBN is embedded in the prognostic system ProCarSur; a prototype of this system is presented. This application shows PBNs as a useful prognostic tool in medical processes. In addition, the article shows the added value of the PBN learning procedure.},\n bibtype = {article},\n author = {},\n journal = {Journal of biomedical informatics},\n number = {6}\n}","author_short":[],"bibbaseid":"prognosticbayesiannetworksiianapplicationinthedomainofcardiacsurgery-2007","role":"author","urls":{},"downloads":0},"bibtype":"article","biburl":null,"creationDate":"2015-04-11T20:28:32.435Z","downloads":0,"keywords":[],"search_terms":["prognostic","bayesian","networks","application","domain","cardiac","surgery"],"title":"Prognostic Bayesian networks II: an application in the domain of cardiac surgery.","year":2007}