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A comparison of methods for assessing penetrating trauma on retrospective multi-center data
Journal article   Open access   Peer reviewed

A comparison of methods for assessing penetrating trauma on retrospective multi-center data

Bilal A. Ahmed, Michael E. Matheny, Phillip L. Rice, John R. Clarke and Omolola I. Ogunyemi
Journal of biomedical informatics, v 42(2)
Apr 2009
DOI: https://doi.org/10.1016/j.jbi.2008.09.002
PMID: 18929685
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1016/j.jbi.2008.09.002View
Published, Version of Record (VoR)Open Access (Publisher-Specific),  Open

Abstract

Artificial intelligence Computer-assisted diagnosis Gunshot wounds Penetrating trauma Stab wounds
TraumaSCAN-Web (TSW) is a computerized decision support system for assessing chest and abdominal penetrating trauma which utilizes 3D geometric reasoning and a Bayesian network with subjective probabilities obtained from an expert. The goal of the present study is to determine whether a trauma risk prediction approach using a Bayesian network with a predefined structure and probabilities learned from penetrating trauma data is comparable in diagnostic accuracy to TSW. Parameters for two Bayesian networks with expert-defined structures were learned from 637 gunshot and stab wound cases from three hospitals, and diagnostic accuracy was assessed using 10-fold cross-validation. The first network included information on external wound locations, while the second network did not. Diagnostic accuracy of learned networks was compared to that of TSW on 194 previously evaluated cases. For 23 of the 24 conditions modeled by TraumaSCAN-Web, 16 conditions had Areas Under the ROC Curve (AUCs) greater than 0.90 while 21 conditions had AUCs greater than 0.75 for the first network. For the second network, 16 and 20 conditions had AUCs greater than 0.90 and 0.75, respectively. AUC results for learned networks on 194 previously evaluated cases were better than or equal to AUC results for TSW for all diagnoses evaluated except diaphragm and heart injuries. For 23 of the 24 penetrating trauma conditions studied, a trauma diagnosis approach using Bayesian networks with predefined structure and probabilities learned from penetrating trauma data was better than or equal in diagnostic accuracy to TSW. In many cases, information on wound location in the first network did not significantly add to predictive accuracy. The study suggests that a decision support approach that uses parameter-learned Bayesian networks may be sufficient for assessing some penetrating trauma conditions.

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7 citations in Web of Science
6 citations in Scopus

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#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Interdisciplinary Applications
Medical Informatics
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