Ahmed Bilal A, Matheny Michael E, Rice Phillip L, Clarke John R, Ogunyemi Omolola I
University of Toronto, Faculty of Medicine, Toronto, Ont., Canada.
J Biomed Inform. 2009 Apr;42(2):308-16. doi: 10.1016/j.jbi.2008.09.002. Epub 2008 Oct 1.
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.
创伤扫描网络（TSW）是一种用于评估胸部和腹部穿透伤的计算机化决策支持系统，它利用三维几何推理和一个带有从专家处获得的主观概率的贝叶斯网络。本研究的目的是确定一种使用具有预定义结构和从穿透伤数据中学习到的概率的贝叶斯网络的创伤风险预测方法在诊断准确性上是否与TSW相当。
从三家医院的637例枪伤和刺伤病例中学习两个具有专家定义结构的贝叶斯网络的参数，并使用10折交叉验证评估诊断准确性。第一个网络包含外部伤口位置的信息，而第二个网络不包含。将学习到的网络的诊断准确性与TSW在194例先前评估病例上的诊断准确性进行比较。
对于创伤扫描网络建模的24种情况中的23种，第一个网络中16种情况的ROC曲线下面积（AUC）大于0.90，21种情况的AUC大于0.75。对于第二个网络，分别有16种和20种情况的AUC大于0.90和0.75。在194例先前评估病例上，学习到的网络的AUC结果在除膈肌和心脏损伤外的所有评估诊断中均优于或等于TSW的AUC结果。
对于所研究的24种穿透伤情况中的23种，使用具有预定义结构和从穿透伤数据中学习到的概率的贝叶斯网络的创伤诊断方法在诊断准确性上优于或等同于TSW。在许多情况下，第一个网络中伤口位置的信息并没有显著提高预测准确性。该研究表明，使用参数学习的贝叶斯网络的决策支持方法可能足以评估某些穿透伤情况。
