使用SHapley Additive exPlanations可解释的机器学习模型预测肺炎支原体肺炎中的肺不张
{"_":"Prediction of atelectasis in pneumonia using a SHapley Additive exPlanations-interpretable machine learning model.","i":["Mycoplasma pneumoniae"]}
摘要(中文译文)
本研究旨在评估三种机器学习模型——K近邻(KNN)、支持向量机(SVM)和神经网络(NN)——在预测肺炎支原体肺炎(MPP)患儿发生肺不张风险方面的性能。本研究还引入SHAP(SHapley Additive exPlanations)分析以解释模型预测。基于508例儿科患者的临床数据,我们进行了特征选择并构建了KNN、SVM和NN模型。在独立验证集上比较了模型性能,并采用SHAP值阐明模型的预测逻辑。在验证集上,神经网络(NN)模型表现出最佳的整体性能,AUC为0.89,准确率为0.82。KNN模型表现出相当的性能(AUC=0.88),而SVM模型达到了最高的特异度(0.87)。SHAP分析一致识别出中性粒细胞百分比(NEU.pct)、血清淀粉样蛋白A(SAA)和C反应蛋白(CRP)为影响预测的最关键变量。本研究表明不同机器学习模型在预测MPP肺不张风险方面的有效性。神经网络尤其因其强大的非线性建模能力而表现出优越的性能。这些可解释的模型为临床医生提供了一组多样化的工具,以适应不同的临床优先需求,如整体准确性或高特异度,从而促进高危儿童的早期识别和分层管理。
Abstract
This study aimed to evaluate the performance of three machine learning models-K-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN)-for predicting the risk of atelectasis in children with pneumonia (MPP). The study also incorporated SHAP (SHapley Additive exPlanations) analysis to interpret model predictions. Based on the clinical data of 508 pediatric patients, we performed feature selection and developed KNN, SVM, and NN models. Model performance was compared on an independent validation set, and SHAP values were employed to elucidate the predictive logic of the models. On the validation set, the neural network (NN) model demonstrated the best overall performance, with an AUC of 0.89 and an accuracy of 0.82. The KNN model showed comparable performance (AUC = 0.88), while the SVM model achieved the highest specificity (0.87). The SHAP analysis consistently identified neutrophil percentage (NEU.pct), serum amyloid A (SAA), and C-reactive protein (CRP) as the most critical variables influencing the predictions. This study demonstrates the effectiveness of different machine learning models in predicting the risk of atelectasis in MPP. The neural network, in particular, exhibited superior performance owing to its powerful non-linear modeling capabilities. These interpretable models provide clinicians with a diverse set of tools to accommodate various clinical priorities, such as overall accuracy or high specificity, thereby facilitating the early identification and stratified management of high-risk children.
如何引用
AMA
Jia Sun, Tengfei Wang, Mengsi Li, Mian Wang. {"_":"Prediction of atelectasis in pneumonia using a SHapley Additive exPlanations-interpretable machine learning model.","i":["Mycoplasma pneumoniae"]}. Frontiers in pediatrics. 2026; doi:10.3389/fped.2026.1747948.
APA
Jia Sun, Tengfei Wang, Mengsi Li, Mian Wang (2026). {"_":"Prediction of atelectasis in pneumonia using a SHapley Additive exPlanations-interpretable machine learning model.","i":["Mycoplasma pneumoniae"]}. Frontiers in pediatrics. https://doi.org/10.3389/fped.2026.1747948
GB/T 7714
Jia Sun, Tengfei Wang, Mengsi Li, Mian Wang. {"_":"Prediction of atelectasis in pneumonia using a SHapley Additive exPlanations-interpretable machine learning model.","i":["Mycoplasma pneumoniae"]}[J]. Frontiers in pediatrics, 2026 doi:10.3389/fped.2026.1747948.
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