Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model.
Prediction of Acute Kidney Injury after Liver Transplantation: Machine Learning Approaches vs. Logistic Regression Model.
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DOI：
10.3390/jcm7110428
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发表时间：
2018-11-08
影响因子：
3.9
通讯作者：
Lee KH
中科院分区：
文献类型：
--
作者：
Lee HC;Yoon SB;Yang SM;Kim WH;Ryu HG;Jung CW;Suh KS;Lee KH
据报道，肝移植后急性肾损伤（AKI）与死亡率增加有关。最近，据报道机器学习方法比传统的统计分析具有更好的预测能力。我们比较了机器学习方法与逻辑回归分析的性能，以预测肝移植后 AKI。我们回顾了 1211 名患者，获得了术前和术中麻醉和手术相关的变量。主要结局是根据急性肾损伤网络标准定义的术后 AKI。使用了以下机器学习技术：决策树、随机森林、梯度提升机、支持向量机、朴素贝叶斯、多层感知器和深度信念网络。将这些技术与接受者操作特征曲线下面积 (AUROC) 的逻辑回归分析进行比较。 365 名患者 (30.1%) 出现 AKI。在预测所有阶段 AKI（0.90，95% 置信区间 [CI] 0.86–0.93）或 2 或 3 阶段 AKI 的所有分析中，AUROC 的性能在梯度增强机中表现最佳。逻辑回归分析的 AUROC 为 0.61 (95% CI 0.56–0.66)。决策树和随机森林技术表现出中等性能（AUROC 分别为 0.86 和 0.85）。支持向量机、朴素贝叶斯、神经网络和深度信念网络的AUROC小于其他模型。在我们对七种机器学习方法与逻辑回归分析进行比较时，梯度增强机表现出最佳性能，具有最高的 AUROC。基于我们的梯度提升模型开发了基于互联网的风险估计器。然而，需要前瞻性研究来验证我们的结果。
Acute kidney injury (AKI) after liver transplantation has been reported to be associated with increased mortality. Recently, machine learning approaches were reported to have better predictive ability than the classic statistical analysis. We compared the performance of machine learning approaches with that of logistic regression analysis to predict AKI after liver transplantation. We reviewed 1211 patients and preoperative and intraoperative anesthesia and surgery-related variables were obtained. The primary outcome was postoperative AKI defined by acute kidney injury network criteria. The following machine learning techniques were used: decision tree, random forest, gradient boosting machine, support vector machine, naïve Bayes, multilayer perceptron, and deep belief networks. These techniques were compared with logistic regression analysis regarding the area under the receiver-operating characteristic curve (AUROC). AKI developed in 365 patients (30.1%). The performance in terms of AUROC was best in gradient boosting machine among all analyses to predict AKI of all stages (0.90, 95% confidence interval [CI] 0.86–0.93) or stage 2 or 3 AKI. The AUROC of logistic regression analysis was 0.61 (95% CI 0.56–0.66). Decision tree and random forest techniques showed moderate performance (AUROC 0.86 and 0.85, respectively). The AUROC of support the vector machine, naïve Bayes, neural network, and deep belief network was smaller than that of the other models. In our comparison of seven machine learning approaches with logistic regression analysis, the gradient boosting machine showed the best performance with the highest AUROC. An internet-based risk estimator was developed based on our model of gradient boosting. However, prospective studies are required to validate our results.
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影响因子：
8.8
作者：
Lee CK;Hofer I;Gabel E;Baldi P;Cannesson M
通讯作者：
Cannesson M
影响因子：
5.2
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Kasbekar PU;Goel P;Jadhav SP
通讯作者：
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通讯作者：
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作者：
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通讯作者：
Mantz, Jean
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作者：
Adelmann, Dieter;Kronish, Kate;Ramsay, Michael A
通讯作者：
Ramsay, Michael A