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Bayesian decision trees for predicting survival of patients: a study on the US National Trauma Data Bank

Research Output: Contribution to journal Article Peer-review

Open access

Abstract

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 602-612

Journal (Volume, Issue Number)

Computer Methods and Programs in Biomedicine (Volume 111, Issue 3)

Publication milestones

  • Published - 01/01/2013

Publication status

Published - 01/01/2013

ISSN

0169-2607

External Publication IDs

  • handle.net: 10547/293690
  • Scopus: 84880587151

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