Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy
Machine learning prediction of stone-free success in patients with urinary stone after treatment of shock wave lithotripsy
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DOI：
10.1186/s12894-020-00662-x
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发表时间：
2020-07-03
期刊：
影响因子：
2
通讯作者：
Song, Ki Hak
中科院分区：
文献类型：
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作者：
Yang, Seung Woo;Hyon, Yun Kyong;Song, Ki Hak
背景：本研究的目的是确定决策支持分析对冲击波碎石术成功率的预测价值，并利用机器学习方法分析冲击波碎石术患者的数据，以评估影响预后的因素。方法回顾2015-2018年间接受SWL治疗的358例尿路结石(肾结石和输尿管上段结石)患者的临床资料，评价其可能的预后特征，包括患者群体特征、非对比CT图像上的尿路结石特征。我们进行了80%的训练集和20%的测试集来预测成功，主要使用了基于决策树的机器学习算法，如随机森林(RF)、极端梯度增强树(XGBoost)和光梯度增强方法(LightGBM)。结果在机器学习分析中，RF、XGBoost和LightGBM对结石排出的预测准确率分别为86.0、87.5和87.9%，对一次取石成功率的预测准确率分别为78.0、77.4和77.0%。在对无结石的预测中，LightGBM产生了最好的准确性，RF产生了在这些方法中一次成功的最好的准确性。机器学习分析的敏感度和特异度分别为(0.74~0.78和0.92~0.93)无结石和(0.79~0.81和0.74~0.75)一次成功。机器学习分析的曲线下面积(AUC值)分别为(0.84~0.85)和(0.77~0.78)，其95%可信区间(CI)分别为(0.730~0.933)和(0.673~0.866)。结论我们应用选择的机器学习分析来预测SWL治疗尿路结石的效果。评估了基于机器学习的预测模型约88%的准确率。机器学习算法的重要性可以给出与领域知识相匹配的关于SWL成功结果的有效和影响因素的见解。
Background The aims of this study were to determine the predictive value of decision support analysis for the shock wave lithotripsy (SWL) success rate and to analyze the data obtained from patients who underwent SWL to assess the factors influencing the outcome by using machine learning methods. Methods We retrospectively reviewed the medical records of 358 patients who underwent SWL for urinary stone (kidney and upper-ureter stone) between 2015 and 2018 and evaluated the possible prognostic features, including patient population characteristics, urinary stone characteristics on a non-contrast, computed tomographic image. We performed 80% training set and 20% test set for the predictions of success and mainly used decision tree-based machine learning algorithms, such as random forest (RF), extreme gradient boosting trees (XGBoost), and light gradient boosting method (LightGBM). Results In machine learning analysis, the prediction accuracies for stone-free were 86.0, 87.5, and 87.9%, and those for one-session success were 78.0, 77.4, and 77.0% using RF, XGBoost, and LightGBM, respectively. In predictions for stone-free, LightGBM yielded the best accuracy and RF yielded the best one in those for one-session success among those methods. The sensitivity and specificity values for machine learning analytics are (0.74 to 0.78 and 0.92 to 0.93) for stone-free and (0.79 to 0.81 and 0.74 to 0.75) for one-session success, respectively. The area under curve (AUC) values for machine learning analytics are (0.84 to 0.85) for stone-free and (0.77 to 0.78) for one-session success and their 95% confidence intervals (CIs) are (0.730 to 0.933) and (0.673 to 0.866) in average of methods, respectively. Conclusions We applied a selected machine learning analysis to predict the result after treatment of SWL for urinary stone. About 88% accurate machine learning based predictive model was evaluated. The importance of machine learning algorithm can give matched insights to domain knowledge on effective and influential factors for SWL success outcomes.