Random forest can predict 30-day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination
Random forest can predict 30-day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination
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DOI：
10.1111/j.1468-1331.2010.02955.x
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发表时间：
2010-07-01
影响因子：
5.1
通讯作者：
Tseng, K. -H.
中科院分区：
文献类型：
--
作者：
Peng, S. -Y.;Chuang, Y. -C.;Tseng, K. -H.
背景和目的：基于患者和疾病特征的风险分层模型有助于临床决策和比较不同医生或医院之间的护理质量。此外，死亡率的预测有利于优化资源利用。我们评估的准确性和鉴别力的随机森林（RF），以预测30天的死亡率自发性脑出血（SICH）。方法：我们回顾性研究了423例患者入住台中荣民总医院被诊断为自发性脑出血24小时内中风发作。使用患者的初始评估数据来训练RF模型。使用受试者工作特征曲线（AUC）下的面积来量化预测性能。的RF模型的性能进行了比较，人工神经网络（ANN），支持向量机（SVM），逻辑回归模型，和ICH score.Results：RF预测死亡率的患者与SICH的总体准确率为78.5%。敏感性为79.0%，特异性为78.4%。AUC如下：RF，0.87（0.84-0.90）; ANN，0.81（0.77-0.85）; SVM，0.79（0.75-0.83）; logistic回归，0.78（0.74-0.82）; ICH评分，0.72（0.68-0.76）。RF的判别能力是上级优于其他的预测models.Conclusions：RF提供了最好的预测性能之间的所有测试模型。我们认为RF是临床医生用于预测SICH患者30天死亡率的合适工具。
Background and purpose:Risk-stratification models based on patient and disease characteristics are useful for aiding clinical decisions and for comparing the quality of care between different physicians or hospitals. In addition, prediction of mortality is beneficial for optimizing resource utilization. We evaluated the accuracy and discriminating power of the random forest (RF) to predict 30-day mortality of spontaneous intracerebral hemorrhage (SICH).Methods:We retrospectively studied 423 patients admitted to the Taichung Veterans General Hospital who were diagnosed with spontaneous SICH within 24 h of stroke onset. The initial evaluation data of the patients were used to train the RF model. Areas under the receiver operating characteristic curves (AUC) were used to quantify the predictive performance. The performance of the RF model was compared to that of an artificial neural network (ANN), support vector machine (SVM), logistic regression model, and the ICH score.Results:The RF had an overall accuracy of 78.5% for predicting the mortality of patients with SICH. The sensitivity was 79.0%, and the specificity was 78.4%. The AUCs were as follows: RF, 0.87 (0.84-0.90); ANN, 0.81 (0.77-0.85); SVM, 0.79 (0.75-0.83); logistic regression, 0.78 (0.74-0.82); and ICH score, 0.72 (0.68-0.76). The discriminatory power of RF was superior to that of the other prediction models.Conclusions:The RF provided the best predictive performance amongst all of the tested models. We believe that the RF is a suitable tool for clinicians to use in predicting the 30-day mortality of patients after SICH.