...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of stroke and cerebrovascular diseases: The official journal of National Stroke Association >The Prediction of Malignant Middle Cerebral Artery Infarction: A Predicting Approach Using Random Forest
【24h】

The Prediction of Malignant Middle Cerebral Artery Infarction: A Predicting Approach Using Random Forest

机译：恶性中脑动脉梗死的预测：随机森林的预测方法

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Background: Malignant middle cerebral artery infarction (MMI) is always associated with high mortality rates. Early decompressive craniectomy is crucial to its treatment. The purpose of this study was to establish a reliable model for an early prediction of MMI. Methods: Using a retrospective survey, we have collected the data of 132 patients with middle cerebral artery infarction. According to a prognosis, the patients are divided into the MMI group (n = 36) and the non-MMI group (n = 96). All the patients are represented by their clinical, biochemical, and imaging features. Then a random forest (RF) prediction model is established on the clinical data. Meanwhile, 3 traditional prediction models, including univariate linear discriminant analysis (LDA) model, multivariate LDA model, and binary logistic regression analysis (BLRA), are built to compare with the RF model. The prediction performance of different models is assessed by the area under the receiver operating characteristic curves (AUCs). Results: Four parameters, Glasgow Coma Scale, midline shifting, area, and volume of focus, selected as predictors in all models. As independent predictors, their AUCs are .72-.80, and when the sensitivities are high (.91-.95), the specificities are low (.32-.53). The AUC of RF model is .96, 95% confidence interval (CI) is (.93-.99), sensitivity is 1, and specificity is .85. The AUC of the multivariate LDA model is .87 and 95% CI is (.80-.93). The AUC of the BLRA model is .86 and 95% CI is (.80-.93). Conclusions: The RF performs very well in the given clinical data set, which indicates that the RF is applicable to the early prediction of the MMI.
机译：背景：恶性大脑中动脉梗塞（MMI）总是与高死亡率相关。早期减压颅骨切除术对其治疗至关重要。这项研究的目的是为MMI的早期预测建立一个可靠的模型。方法：通过回顾性调查，我们收集了132例脑中动脉梗死患者的数据。根据预后，将患者分为MMI组（n = 36）和非MMI组（n = 96）。所有患者均以其临床，生化和影像学特征为代表。然后根据临床数据建立随机森林（RF）预测模型。同时，建立了3个传统的预测模型，包括单变量线性判别分析（LDA）模型，多元LDA模型和二进制逻辑回归分析（BLRA），以与RF模型进行比较。不同模型的预测性能由接收器工作特性曲线（AUC）下的面积评估。结果：在所有模型中均选择了四个参数，格拉斯哥昏迷评分，中线移位，面积和焦点体积作为预测指标。作为独立的预测因子，它们的AUC为0.72-.80，而敏感性高时（.91-.95），特异性低（.32-.53）。 RF模型的AUC为0.96，95％置信区间（CI）为（.93-.99），灵敏度为1，特异性为0.85。多元LDA模型的AUC为0.87，95％CI为（.80-.93）。 BLRA模型的AUC为.86，95％CI为（.80-.93）。结论：RF在给定的临床数据集中表现良好，这表明RF适用于MMI的早期预测。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号