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dc.contributor.authorSchetinin, Vitalyen_GB
dc.contributor.authorJakaite, Livijaen_GB
dc.contributor.authorJakaitis, Janisen_GB
dc.contributor.authorKrzanowski, Wojteken_GB
dc.date.accessioned2013-06-10T09:46:24Z
dc.date.available2013-06-10T09:46:24Z
dc.date.issued2013
dc.identifier.citationSchetinin, V., Jakaite, L., Jakaitis, J., Krzanowski, W. (2013) 'Bayesian Decision Trees for predicting survival of patients: a study on the US National Trauma Data Bank', Computer Methods and Programs in Biomedicine, 111(3), pp. 602- 612en_GB
dc.identifier.issn0169-2607
dc.identifier.doi10.1016/j.cmpb.2013.05.015
dc.identifier.urihttp://hdl.handle.net/10547/293690
dc.description.abstractTrauma and Injury Severity Score (TRISS) models have been developed for predicting the survival probability of injured patients the majority of which obtain up to three injuries in six body regions. Practitioners have noted that the accuracy of TRISS predictions is unacceptable for patients with a larger number of injuries. Moreover, the TRISS method is incapable of providing accurate estimates of predictive density of survival, that are required for calculating confidence intervals. In this paper we propose Bayesian in ference for estimating the desired predictive density. The inference is based on decision tree models which split data along explanatory variables, that makes these models interpretable. The proposed method has outperformed the TRISS method in terms of accuracy of prediction on the cases recorded in the US National Trauma Data Bank. The developed method has been made available for evaluation purposes as a stand-alone application.
dc.language.isoenen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0169260713001727
dc.subjectG760 Machine Learningen_GB
dc.subjectG311 Medical Statisticsen_GB
dc.subjectBayesian predictionen_GB
dc.subjectsurvival probabilityen_GB
dc.subjectMarkov chain Monte Carloen_GB
dc.subjectclassification treeen_GB
dc.subjecttrauma careen_GB
dc.titleBayesian decision trees for predicting survival of patients: a study on the US National Trauma Data Banken
dc.typeArticleen
dc.contributor.departmentUniversity of Bedfordshireen_GB
dc.contributor.departmentUniversity of Exeteren_GB
dc.identifier.journalComputer Methods and Programs in Biomedicineen_GB
refterms.dateFOA2020-04-23T08:40:59Z
html.description.abstractTrauma and Injury Severity Score (TRISS) models have been developed for predicting the survival probability of injured patients the majority of which obtain up to three injuries in six body regions. Practitioners have noted that the accuracy of TRISS predictions is unacceptable for patients with a larger number of injuries. Moreover, the TRISS method is incapable of providing accurate estimates of predictive density of survival, that are required for calculating confidence intervals. In this paper we propose Bayesian in ference for estimating the desired predictive density. The inference is based on decision tree models which split data along explanatory variables, that makes these models interpretable. The proposed method has outperformed the TRISS method in terms of accuracy of prediction on the cases recorded in the US National Trauma Data Bank. The developed method has been made available for evaluation purposes as a stand-alone application.


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