A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants.
A deep neural network framework to derive interpretable decision rules for accurate traumatic brain injury identification of infants.
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DOI：
10.1186/s12911-023-02155-x
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发表时间：
2023-04-06
影响因子：
3.5
通讯作者：
中科院分区：
文献类型：
--
作者：
我们的目标是建立一个强大的框架来模拟临床特征与2岁以下儿童创伤性脑损伤（TBI）风险之间的复杂关联，并确定重要特征，以得出临床决策规则，以进行分诊决策。在这项回顾性研究中，我们比较了四种常用的机器学习模型，即支持向量机（SVM）、随机森林（RF）、深度神经网络（DNN）和XGBoost (XGB)，通过使用来自儿科急诊应用研究网络（PECARN）研究的公开数据集，在排列特征重要性测试（PermFIT）框架下，从24个与2岁以下儿童TBI风险相关的输入特征中识别出重要的临床特征。由于CT扫描是诊断TBI的金标准，因此通过将预测的TBI状态与CT扫描结果进行比较来确定预测的准确性。在显著性水平下，DNN、RF、XGB和SVM分别识别出9个、1个、2个和4个显著特征。在准确率（accuracy）、曲线下面积（AUC）和精确查全率曲线下面积（PR-AUC）的比较中，DNN模型的排列特征重要性检验是识别显著特征最强大的框架，优于RF、XGB和SVM等方法，准确率、AUC和PR-AUC分别为0.915、0.794和0.974。这些结果表明，PermFIT-DNN框架可以可靠地识别与TBI状态相关的重要临床特征，并提高预测性能。研究结果可用于为临床决策工具的开发提供信息，旨在为分诊决策提供信息。
We aimed to develop a robust framework to model the complex association between clinical features and traumatic brain injury (TBI) risk in children under age two, and identify significant features to derive clinical decision rules for triage decisions. In this retrospective study, four frequently used machine learning models, i.e., support vector machine (SVM), random forest (RF), deep neural network (DNN), and XGBoost (XGB), were compared to identify significant clinical features from 24 input features associated with the TBI risk in children under age two under the permutation feature importance test (PermFIT) framework by using the publicly available data set from the Pediatric Emergency Care Applied Research Network (PECARN) study. The prediction accuracy was determined by comparing the predicted TBI status with the computed tomography (CT) scan results since CT scan is the gold standard for diagnosing TBI. At a significance level of , DNN, RF, XGB, and SVM identified 9, 1, 2, and 4 significant features, respectively. In a comparison of accuracy (Accuracy), the area under the curve (AUC), and the precision-recall area under the curve (PR-AUC), the permutation feature importance test for DNN model was the most powerful framework for identifying significant features and outperformed other methods, i.e., RF, XGB, and SVM, with Accuracy, AUC, and PR-AUC as 0.915, 0.794, and 0.974, respectively. These results indicate that the PermFIT-DNN framework robustly identifies significant clinical features associated with TBI status and improves prediction performance. The findings could be used to inform the development of clinical decision tools designed to inform triage decisions.
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影响因子：
5.2
作者：
Dunning, J.;Daly, J. Patrick;Mackway-Jones, K.
通讯作者：
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作者：
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