4.6 Article

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

期刊

JOURNAL OF BIOMEDICAL INFORMATICS
卷 42, 期 2, 页码 308-316

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2008.09.002

关键词

Artificial intelligence; Computer-assisted diagnosis; Stab wounds; Gunshot wounds; Penetrating trauma

资金

  1. National Library of Medicine [1R01LM07167, 1T15LM07092]
  2. CREMS Summer Program Award at the University of Toronto

向作者/读者索取更多资源

Objective: 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. Tire 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. Methods: 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 oil 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. Results: 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. Conclusions: 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 Oil 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. (C) 2008 Elsevier Inc. All rights reserved.

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