Bayesian averaging over Decision Tree models for trauma severity scoring
Bayesian predictive modelling
predictive posterior distribution
injury severity scoring
MetadataShow full item record
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”.
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as Green - can archive pre-print and post-print or publisher's version/PDF
- Bayesian averaging over decision tree models: An application for estimating uncertainty in trauma severity scoring.
- Authors: Schetinin V, Jakaite L, Krzanowski W
- Issue date: 2018 Apr
- Bayesian Decision Trees for predicting survival of patients: a study on the US National Trauma Data Bank.
- Authors: Schetinin V, Jakaite L, Jakaitis J, Krzanowski W
- Issue date: 2013 Sep
- Confident interpretation of Bayesian decision tree ensembles for clinical applications.
- Authors: Schetinin V, Fieldsend JE, Partridge D, Coats TJ, Krzanowski WJ, Everson RM, Bailey TC, Hernandez A
- Issue date: 2007 May
- Bayesian logistic injury severity score: a method for predicting mortality using international classification of disease-9 codes.
- Authors: Burd RS, Ouyang M, Madigan D
- Issue date: 2008 May
- Has TRISS become an anachronism? A comparison of mortality between the National Trauma Data Bank and Major Trauma Outcome Study databases.
- Authors: Rogers FB, Osler T, Krasne M, Rogers A, Bradburn EH, Lee JC, Wu D, McWilliams N, Horst MA
- Issue date: 2012 Aug