Bayesian decision trees for predicting survival of patients: a study on the US National Trauma Data Bank
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2013Subjects
G760 Machine LearningG311 Medical Statistics
Bayesian prediction
survival probability
Markov chain Monte Carlo
classification tree
trauma care
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Trauma 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.Citation
Schetinin, 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- 612Additional Links
http://linkinghub.elsevier.com/retrieve/pii/S0169260713001727Type
ArticleLanguage
enISSN
0169-2607ae974a485f413a2113503eed53cd6c53
10.1016/j.cmpb.2013.05.015
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