Bayesian averaging over Decision Tree models for trauma severity scoring

2.50
Hdl Handle:
http://hdl.handle.net/10547/622461
Title:
Bayesian averaging over Decision Tree models for trauma severity scoring
Authors:
Schetinin, Vitaly ( 0000-0003-1826-0153 ) ; Jakaite, Livija; Krzanowski, Wojtek
Abstract:
Health 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.
Citation:
Schetinin V, Jakaite L, Krzanowski W (2018) 'Bayesian averaging over Decision Tree models for trauma severity scoring', Artificial intelligence in medicine 84 139-145
Publisher:
Elsevier
Journal:
Artificial intelligence in medicine
Issue Date:
11-Jan-2018
URI:
http://hdl.handle.net/10547/622461
DOI:
10.1016/j.artmed.2017.12.003
PubMed ID:
29275896
Additional Links:
https://www.sciencedirect.com/science/article/pii/S0933365717301100?via%3Dihub
Type:
Article
Language:
en
ISSN:
0933-3657
EISSN:
0933-3657
Sponsors:
This research was partly supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant GR/R24357/01 “Critical Systems and Data-Driven Technology”.
Appears in Collections:
Computing

Full metadata record

DC FieldValue Language
dc.contributor.authorSchetinin, Vitalyen
dc.contributor.authorJakaite, Livijaen
dc.contributor.authorKrzanowski, Wojteken
dc.date.accessioned2018-01-15T13:26:06Z-
dc.date.available2018-01-15T13:26:06Z-
dc.date.issued2018-01-11-
dc.identifier.citationSchetinin V, Jakaite L, Krzanowski W (2018) 'Bayesian averaging over Decision Tree models for trauma severity scoring', Artificial intelligence in medicine 84 139-145en
dc.identifier.issn0933-3657-
dc.identifier.pmid29275896-
dc.identifier.doi10.1016/j.artmed.2017.12.003-
dc.identifier.urihttp://hdl.handle.net/10547/622461-
dc.description.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.en
dc.description.sponsorshipThis research was partly supported by the UK Engineering and Physical Sciences Research Council (EPSRC) grant GR/R24357/01 “Critical Systems and Data-Driven Technology”.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttps://www.sciencedirect.com/science/article/pii/S0933365717301100?via%3Dihuben
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectBayesian learningen
dc.subjectBayesian predictive modellingen
dc.subjectdecision treesen
dc.subjectpredictive posterior distributionen
dc.subjectinjury severity scoringen
dc.titleBayesian averaging over Decision Tree models for trauma severity scoringen
dc.typeArticleen
dc.identifier.eissn0933-3657-
dc.identifier.journalArtificial intelligence in medicineen
dc.date.updated2018-01-15T13:08:29Z-
dc.description.notepreprint-
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