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首页> 外文会议>The 7th China-Japan-Korea joint symposium on optimization of structural and mechanical systems >Predicting Stroke Outcomes From Physiological Patterns
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Predicting Stroke Outcomes From Physiological Patterns

机译：从生理模式预测中风结果

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摘要

Stroke is one of the major diseases that can cause human deaths. However, despite the frequency and importance of stroke, there are only a limited number of evidence-based acute treatment options currently available. Recent research has indicated that the early changes in common physiological variables represent a potential therapeutic target, thus the manipulation of these variables may eventually yield an effective way to optimise stroke recovery. However, the effects and function domain of those physiological determinants are still unclear. Therefore, developing a relatively accurate prediction method of stroke outcome based on justifiable determinants becomes more and more important to the decision of the medical treatment at the very beginning of the stroke. Although there exist some initial statistical analyses on typical physiological variables, such as blood pressure, glucose, the accuracy is still far from satisfactory. In this work, we take a novel data mining based method to find correlations between physiological parameters of stroke patients, during 48 hours after stroke, and their 3-months stroke outcomes. Our approach not only consider statistical parameters of physiological data, but also include trend analyses of physiological data changes. We test our methods on a real data set of stroke patients registered at Royal Brisbane and Women's Hospital, Australia. Experiment results demonstrated that compared against methods only considering statistical variables, our methods can reach a high precision accuracy, 93.8%, and also a high recall accuracy, 87.4%.
机译：中风是可导致人类死亡的主要疾病之一。然而，尽管中风的频率和重要性，但目前仅有数量有限的基于证据的急性治疗选择。最近的研究表明，常见生理变量的早期变化代表了潜在的治疗目标，因此，对这些变量的操纵可能最终产生一种优化卒中恢复的有效方法。但是，这些生理决定因素的作用和功能范围仍不清楚。因此，基于合理的决定因素开发相对准确的中风预后预测方法对于中风开始时的医疗决策变得越来越重要。尽管已经对一些典型的生理变量（例如血压，葡萄糖）进行了初步的统计分析，但其准确性仍远远不能令人满意。在这项工作中，我们采用一种新颖的基于数据挖掘的方法来发现中风患者在中风后48小时内的生理参数与其3个月中风结果之间的相关性。我们的方法不仅考虑生理数据的统计参数，还包括对生理数据变化的趋势分析。我们在澳大利亚皇家布里斯班妇女医院注册的中风患者的真实数据集上测试了我们的方法。实验结果表明，与仅考虑统计变量的方法相比，我们的方法可以达到93.8％的高精度准确性，以及87.4％的高召回率准确性。

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