This work presents a novel and promising approach to the clinical management of acute stroke. Using machine learning techniques, our research has succeeded in developing accurate diagnosis and prediction real-time models from hemodynamic data. These models are able to diagnose stroke subtype with 30 min of monitoring, to predict the exitus during the first 3 h of monitoring, and to predict the stroke recurrence in just 15 min of monitoring. Patients with difficult access to a CT scan and all patients that arrive at the stroke unit of a specialized hospital will benefit from these positive results. The results obtained from the real-time developed models are the following: stroke diagnosis around 98% precision (97.8% sensitivity, 99.5% specificity), exitus prediction with 99.8% precision (99.8% Sens., 99.9% Spec.), and 98% precision predicting stroke recurrence (98% Sens., 99% Spec.).

这项工作为急性卒中的临床管理提供了一种新颖且有前景的方法。使用机器学习技术，我们的研究成功地从血液动力学数据中开发出准确的诊断和预测实时模型。这些模型能够在 30 分钟的监测中诊断中风亚型，在监测的前 3 小时内预测退出，并在仅 15 分钟的监测中预测中风复发。难以进行 CT 扫描的患者以及所有到达专科医院卒中单元的患者都将从这些阳性结果中受益。从实时开发的模型中获得的结果如下： 中风诊断准确度约为 98 %（灵敏度为97.8 %，灵敏度为 99.5 %特异性），退出预测具有 99.8 % 的精确度（99.8 % Sens.，99.9 % Spec.），以及 98 % 的预测卒中复发的精确度（98 % Sens.，99 % Spec.）。