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Prediction of survival probabilities with Bayesian Decision Trees

Research Output:
Contribution to journal
Article
 Peer-review

Open access

Abstract

Practitioners use Trauma and Injury Severity Score (TRISS) models for predicting the survival probability of an injured patient. The accuracy of TRISS predictions is acceptable for patients with up to three typical injuries, but unacceptable for patients with a larger number of injuries or with atypical injuries. Based on a regression model, the TRISS methodology does not provide the predictive density required for accurate assessment of risk. Moreover, the regression model is difficult to interpret. We therefore consider Bayesian inference for estimating the predictive distribution of survival. The inference is based on decision tree models which recursively split data along explanatory variables, and so practitioners can understand these models. We propose the Bayesian method for estimating the predictive density and show that it outperforms the TRISS method in terms of both goodness-of-fit and classification accuracy. 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 5466-5476

Journal (Volume, Issue Number)

Expert Systems with Applications (Volume 40, Issue 14)

Publication milestones

  • Published - 01/01/2013

Publication status

Published - 01/01/2013

ISSN

0957-4174

Publication IDs

  • handle.net: 10547/293062
  • Scopus: 84877868959

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