目的:本研究旨在开发和验证基于机器学习算法的模型,用于预测糖尿病患者的侵袭性肺炎克雷伯菌肝脓肿综合征(IKPLAS),并比较不同模型的性能。
方法:收集213例糖尿病合并肺炎克雷伯菌肝脓肿患者的临床体征和入院数据作为变量。筛选出最优特征变量,然后是人工神经网络,支持向量机,Logistic回归,随机森林,K-最近的邻居,决策树,建立了XGBoost模型。最后,通过ROC曲线评估模型的预测性能,敏感度(召回),特异性,准确度,精度,F1分数,平均精度,校正曲线,和DCA曲线。
结果:血红蛋白的四个特征,血小板,D-二聚体,通过递归消除方法筛选SOFA评分,并基于这些变量建立了7个预测模型。AUC(0.969),F1分数(0.737),在7个模型中,SVM模型的灵敏度(0.875)和AP(0.890)最高。KNN模型显示最高的特异性(1.000)。除了XGB和DT模型高估了IKPLAS风险的发生,其他模型校准曲线与实际观察结果很好地吻合。决策曲线分析表明,当风险阈值在0.4~0.8之间时,SVM模型的净干预率明显高于其他模型。在特征重要性排序中,SOFA评分对模型的影响显着。
结论:通过机器学习算法可以建立糖尿病合并肺炎克雷伯菌侵袭性肝脓肿综合征的有效预测模型。具有潜在的应用价值。
OBJECTIVE: This
study aimed to develop and validate a machine learning algorithm-based model for predicting invasive Klebsiella pneumoniae liver abscess syndrome(IKPLAS) in diabetes mellitus and compare the performance of different models.
METHODS: The clinical signs and data on the admission of 213 diabetic patients with Klebsiella pneumoniae liver abscesses were collected as variables. The optimal feature variables were screened out, and then Artificial Neural Network, Support Vector Machine, Logistic Regression, Random Forest, K-Nearest Neighbor, Decision Tree, and XGBoost models were established. Finally, the model\'s prediction performance was evaluated by the ROC curve, sensitivity (recall), specificity, accuracy, precision, F1-score, Average Precision, calibration curve, and DCA curve.
RESULTS: Four features of hemoglobin, platelet, D-dimer, and SOFA score were screened by the recursive elimination method, and seven prediction models were established based on these variables. The AUC (0.969), F1-Score(0.737), Sensitivity(0.875) and AP(0.890) of the SVM model were the highest among the seven models. The KNN model showed the highest specificity (1.000). Except that the XGB and DT models over-estimates the occurrence of IKPLAS risk, the other models\' calibration curves are a good fit with the actual observed results. Decision Curve Analysis showed that when the risk threshold was between 0.4 and 0.8, the net rate of intervention of the SVM model was significantly higher than that of other models. In the feature importance ranking, the SOFA score impacted the model significantly.
CONCLUSIONS: An effective prediction model of invasion Klebsiella pneumoniae liver abscess syndrome in diabetes mellitus could be established by a machine learning algorithm, which had potential application value.