Construction of Discharge Summaries Classifier

S. Tsumoto, Tomohiro Kimura, H. Iwata, S. Hirano
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引用次数: 2

Abstract

This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrixand generates two dimensional coordinates. By measuring thedistance between categories and the assigned points, ranking of key wordswill be generated. Then, keywords are selected as attributes according to the rank, andtraining example for classifiers will be generated. Finally learning methodsare applied to the training examples. Experimental validation shows that random forest achieved the best performance and the second best was the deep learner with a small difference, but decision tree methods with many keywords performed only a little worse than neural network or deep learning methods.
流量汇总分类器的构建
提出了一种构建出院摘要分类器的方法。首先,对一组摘要进行词法分析,生成术语矩阵。其次,对分类标签进行对应分析,得到项矩阵生成二维坐标;通过测量类别与指定点之间的距离,将生成关键词的排名。然后根据排序选择关键字作为属性,生成分类器的训练样例。最后将学习方法应用到训练实例中。实验验证表明,随机森林方法的性能最好,深度学习方法的性能次之,差异不大,但包含许多关键字的决策树方法的性能仅略差于神经网络或深度学习方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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