Wang Hsueh-Lin, Hsu Wei-Yen, Lee Ming-Hsueh, Weng Hsu-Huei, Chang Sheng-Wei, Yang Jen-Tsung, Tsai Yuan-Hsiung
Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Chiayi, Taiwan.
Department of Information Management, National Chung Cheng University, Chiayi, Taiwan.
Front Neurol. 2019 Aug 21;10:910. doi: 10.3389/fneur.2019.00910. eCollection 2019.
A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome in patients with primary intracerebral hemorrhage (ICH). We retrospectively collected data on demographic characteristics, laboratory studies and imaging findings of 333 patients with primary ICH. The functional outcomes at the 1st and 6th months after ICH were defined by the modified Rankin scale. All of the attributes were used for preprocessing and for automatic model selection with Automatic Waikato Environment for Knowledge Analysis. Confusion matrix and areas under the receiver operating characteristic curves (AUC) were used to test the predictive performance. Among the models tested, the random forest provided the best predictive performance for functional outcome. The overall accuracy for predicting the 1st month outcome was 83.1%, with 77.4% sensitivity and 86.9% specificity, and the AUC was 0.899. The overall accuracy for predicting the 6th month outcome was 83.9%, with 72.5% sensitivity and 90.6% specificity, and the AUC was 0.917. Using an automatic machine learning technique to predict functional outcome after ICH is feasible, and the random forest model provides the best predictive performance across all tested models. This prediction model may provide information regarding functional outcome for clinicians that will help provide appropriate medical care for patients and information for their caregivers.
预测模型可以为医生、患者家属和患者提供有关疾病严重程度及其预测结果的准确信息。在本研究中，我们使用基于自动化机器学习的方法构建了一个预后模型，以预测原发性脑出血（ICH）患者的功能结局。我们回顾性收集了333例原发性ICH患者的人口统计学特征、实验室检查和影像学检查结果。ICH后第1个月和第6个月的功能结局采用改良Rankin量表进行定义。所有属性都用于预处理，并通过怀卡托知识分析自动环境进行自动模型选择。使用混淆矩阵和受试者工作特征曲线下面积（AUC）来测试预测性能。在所测试的模型中，随机森林对功能结局的预测性能最佳。预测第1个月结局的总体准确率为83.1%，敏感性为77.4%，特异性为86.9%，AUC为0.899。预测第6个月结局的总体准确率为83.9%，敏感性为72.5%，特异性为90.6%，AUC为0.917。使用自动机器学习技术预测ICH后的功能结局是可行的，随机森林模型在所有测试模型中提供了最佳的预测性能。该预测模型可为临床医生提供有关功能结局的信息，这将有助于为患者提供适当的医疗护理，并为其护理人员提供信息。
