Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression
Comparative Analysis of Three Machine-Learning Techniques and Conventional Techniques for Predicting Sepsis-Induced Coagulopathy Progression
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DOI：
10.3390/jcm9072113
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发表时间：
2020-07-01
影响因子：
3.9
通讯作者：
Nishida, Osamu
中科院分区：
文献类型：
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作者：
Hasegawa, Daisuke;Yamakawa, Kazuma;Nishida, Osamu
脓毒症引起的凝血病预后较差；然而，目前还没有成熟的预测工具。我们的目标是使用机器学习技术创建凝血功能进展的预测模型，以评估机器学习和传统技术的预测准确性。在日本脓毒性弥散性血管内凝血回顾性研究的基础上进行了事后亚组分析。我们使用国际血栓与止血学会弥散性血管内凝血（DIC）评分计算DIC积分为（（第3天DIC评分）-（第1天DIC评分））。主要结果是确定Delta DIC的预测准确性是否大于0。次要结果是Delta DIC的实际预测准确性（预测Delta DIC-实际Delta DIC）。我们使用了机器学习方法，如随机森林（RF）、支持向量机（SVM）和神经网络（NN）；并将其预测精度与传统方法进行了比较。共纳入1017例患者。对于DIC的进展，多元线性回归、RF、SVM和NN模型的预测准确率分别为63.7%、67.0%、64.4%和59.8%。多元线性回归模型、RF模型、SVM模型和NN模型的预测δ DIC与实际δ DIC的差值分别为2.05、1.54、2.24和1.77。RF的预测准确度最高。
Sepsis-induced coagulopathy has poor prognosis; however, there is no established tool for predicting it. We aimed to create predictive models for coagulopathy progression using machine-learning techniques to evaluate predictive accuracies of machine-learning and conventional techniques. A post-hoc subgroup analysis was conducted based on the Japan Septic Disseminated Intravascular Coagulation retrospective study. We used the International Society on Thrombosis and Haemostasis disseminated intravascular coagulation (DIC) score to calculate the Delta DIC score as ((DIC score on Day 3) - (DIC score on Day 1)). The primary outcome was to determine whether the predictive accuracy of Delta DIC was more than 0. The secondary outcome was the actual predictive accuracy of Delta DIC (predicted Delta DIC-real Delta DIC). We used the machine-learning methods, such as random forests (RF), support vector machines (SVM), and neural networks (NN); their predictive accuracies were compared with those of conventional methods. In total, 1017 patients were included. Regarding DIC progression, predictive accuracy of the multiple linear regression, RF, SVM, and NN models was 63.7%, 67.0%, 64.4%, and 59.8%, respectively. The difference between predicted Delta DIC and real Delta DIC was 2.05, 1.54, 2.24, and 1.77 for the multiple linear regression, RF, SVM, and NN models, respectively. RF had the highest predictive accuracy.