Bayesian averaging over Decision Tree models for trauma severity scoring
Bayesian predictive modelling
predictive posterior distribution
injury severity scoring
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AbstractHealth care practitioners analyse possible risks of misleading decisions and need to estimate and quantify uncertainty in predictions. We have examined the “gold” standard of screening a patient's conditions for predicting survival probability, based on logistic regression modelling, which is used in trauma care for clinical purposes and quality audit. This methodology is based on theoretical assumptions about data and uncertainties. Models induced within such an approach have exposed a number of problems, providing unexplained fluctuation of predicted survival and low accuracy of estimating uncertainty intervals within which predictions are made. Bayesian method, which in theory is capable of providing accurate predictions and uncertainty estimates, has been adopted in our study using Decision Tree models. Our approach has been tested on a large set of patients registered in the US National Trauma Data Bank and has outperformed the standard method in terms of prediction accuracy, thereby providing practitioners with accurate estimates of the predictive posterior densities of interest that are required for making risk-aware decisions.
CitationSchetinin V, Jakaite L, Krzanowski W (2018) 'Bayesian averaging over Decision Tree models for trauma severity scoring', Artificial intelligence in medicine 84 139-145
SponsorsThis research was partly supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant GR/R24357/01 “Critical Systems and Data-Driven Technology”.
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