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Feature extraction from nursing-care texts for classification

机译：从护理文本进行分类的特征提取

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The nursing care quality improvement is very important in the medical field. Currently, nursing-care freestyle texts (nursing-care data) are collected from many hospitals in Japan by using Web applications and stored into the database. Some nursing-care experts evaluate the collected data to improve nursing care quality. For evaluating the nursing-care data, experts need to read all freestyle texts carefully and then classified them into four classes. However, it is a very hard task for each expert to evaluate the data because of huge number of nursing-care data in the database. In order to reduce workloads evaluating nursing-care data, we have proposed a support vector machine (SVM) based classification system. In this paper, to improve the classification performance, we propose a feature extraction method for generating numerical data from collected nursing-care texts. In our proposed method, the frequency in use of a term in the term list is used for selecting features which contribute to the classification. And then, the nursing-care numerical data are classified by the SVM based classification system. From computer simulation results, we show the effectiveness of our proposed method.
机译：护理质量改善在医学领域非常重要。目前，通过使用Web应用程序并存储到数据库中，从日本的许多医院收集护理自由式文本（护理数据）。一些护理专家评估收集的数据，以提高护理质量。为了评估护理数据，专家需要仔细阅读所有自由式文本，然后将它们分为四个类别。但是，由于数据库中的众多护理数据，每个专家都是一个非常艰巨的任务。为了减少评估护理数据的工作负载，我们提出了一种基于支持向量机（SVM）的分类系统。在本文中，为了提高分类性能，我们提出了一种特征提取方法，用于从收集的护理文本产生数值数据。在我们所提出的方法中，术语列表中使用术语的频率用于选择有助于分类的特征。然后，护理数值数据由基于SVM的分类系统分类。从计算机仿真结果，我们展示了我们提出的方法的有效性。

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