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首页> 外文期刊>Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis >Neural Network for Determining Risk Rate of Post-Heart Stroke Patients
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Neural Network for Determining Risk Rate of Post-Heart Stroke Patients

机译:用于确定后心脏中风患者风险率的神经网络

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The ischemic heart disease presents an important health problem that affects a great part of the population and is the cause of one third of all deaths in the Czech Republic. The availability of data describing the patients' prognosis enables their further analysis, with the aim of lowering the patients' risk, by proposing optimum treatment. The main reason for creating the neural network model is not only to automate the process of establishing the risk rate of patients suffering from ischemic heart disease, but also to adapt it for practical use in clinical conditions. Our aim is to identify especially the specific group of risk-rate patients whose well-timed preventive care can improve the quality and prolong the length of their lives. The aim of the paper is to propose a patient-parameter structure, using which we could create a suitable model based on a self-taught neural network. The emphasis is placed on identifying key descriptive parameters (in the form of a reduction of the available descriptive parameters) that are crucial for identifying the required patients, and simultaneously to achieve a portability of the model among individual clinical workplaces (availability of parameters).
机译:缺血性心脏病呈现出一个重要的健康问题,影响人口的大部分,是捷克共和国所有死亡的原因。描述患者预后的数据的可用性使其进一步分析,目的是通过提出最佳处理来降低患者的风险。创建神经网络模型的主要原因不仅是为了自动化建立患有缺血性心脏病患者的风险率的过程,而且为了使其适应临床条件的实际应用。我们的目标是鉴于特别是预防性预防性护理的特定风险率患者的特定群体可以提高质量,延长其生命的长度。本文的目的是提出患者参数结构,我们可以使用它可以基于自学式神经网络创建合适的模型。重点是识别关键描述参数(以可用描述性参数的降低的形式)对识别所需患者至关重要,并且同时实现各个临床工作场所之间模型的可移植性(参数的可用性)。

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