Prediction of survival probabilities with Bayesian Decision Trees |
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Authors: | Vitaly Schetinin Livia Jakaite Wojtek J. Krzanowski |
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Affiliation: | 1. Department of Computer Science and Technology, University of Bedfordshire, Park Squre, Luton, LU1 3JU,UK;2. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, Exeter, EX4 4QF,UK |
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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. |
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Keywords: | Bayesian prediction Survival probability Markov chain Monte Carlo Classification tree Trauma care |
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