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Predicting Clinical Status of Patients After an Acute Ischemic Stroke Using Random Forests

机译:使用随机林预测急性缺血性脑卒中患者的临床状态

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According to the World Health Organization, a stroke has been the second most common cause of death in the world in the last 15 years. An ischemic stroke accounts for almost 80% of all cases. The University Hospital Ostrava in the Czech Republic collects various information about patients who were transported there after suffering from an acute ischemic stroke, such as the affected brain hemisphere, duration of medical procedure or presence of hypertension. The objective of this paper was finding a model which would be able to predict patient's clinical outcome three months after an ischemic stroke based on the collected data. It was also desirable to analyse importance of the considered variables. For this purpose, the random forests algorithm was used. To avoid biased variable importance, we used an alternative approach to the random forests which uses the conditional inference trees. Firstly, the commonly used modified Rankin Scale was used for describing the patient's outcome three months after a stroke. Secondly, only two values for the clinical status were considered, by meaning they correspond with the values 0-3 and 4-6 of modified Rankin Scale. The best performance was achieved with the second approach to description of the clinical outcome with the calculated classification accuracy 86%.
机译:根据世界卫生组织的说法,在过去的15年里,中风是世界上世界上最常见的死亡原因。缺血性卒中占所有病例的近80%。捷克共和国大学医院奥斯特拉虎收集有关在患有急性缺血性卒中后在那里进行的各种信息,例如受影响的脑半球,医疗程序的持续时间或高血压的存在。本文的目的是找到一种模型,该模型将能够在基于所收集的数据的缺血性卒中后三个月预测患者的临床结果。还希望分析所考虑的变量的重要性。为此,使用随机林算法。为避免偏见的变量重要性,我们使用了一种使用条件推理树的随机林的替代方法。首先,常用的改进的rankin规模用于描述中风三个月后的患者的结果。其次,考虑了临床状态的两个值,其含义与改进的Rankin规模的值0-3和4-6相对应。通过计算分类精度的临床结果描述的第二种方法实现了最佳性能。

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