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首页> 外文期刊>Journal of biomedical informatics. >A comparison of methods for assessing penetrating trauma on retrospective multi-center data.
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A comparison of methods for assessing penetrating trauma on retrospective multi-center data.

机译：根据回顾性多中心数据比较评估穿透性创伤的方法。

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摘要

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. 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. 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 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. 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 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.
机译：目的：TraumaSCAN-Web（TSW）是一种用于评估胸部和腹部穿透性创伤的计算机化决策支持系统，该系统利用3D几何推理和贝叶斯网络，并具有从专家那里获得的主观概率。本研究的目的是确定使用具有预定结构和从穿透创伤数据中学到的概率的贝叶斯网络进行的创伤风险预测方法在诊断准确性上是否可与TSW相媲美。方法：从三家医院的637例枪击和刺伤病例中学习了两个具有专家定义结构的贝叶斯网络的参数，并使用10倍交叉验证法评估了诊断准确性。第一个网络包含有关外部伤口位置的信息，而第二个网络则没有。在先前评估的194个案例中，将学习网络的诊断准确性与TSW的诊断准确性进行了比较。结果：对于由TraumaSCAN-Web建模的24个条件中的23个，第一个网络的16个条件的ROC曲线下面积（AUC）大于0.90，而21个条件的AUC大于0.75。对于第二个网络，16个条件和20个条件的AUC分别大于0.90和0.75。对于194例先前评估过的病例，学习网络的AUC结果优于或等于TSW的所有诊断（除evaluated肌和心脏损伤）的AUC结果。结论：对于研究的24种穿透性创伤情况中的23种，使用具有预定结构和从穿透性创伤数据中获悉的概率的贝叶斯网络进行的创伤诊断方法，其诊断准确性优于或等于TSW。在许多情况下，有关第一个网络中伤口位置的信息并未显着增加预测准确性。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性创伤情况。

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