Injury Narrative Text Classification: A Preliminary Study

Lin Chen, K. Vallmuur, R. Nayak
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Abstract

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.
伤害叙事文本分类的初步研究
医院急诊科以叙事文本的形式记录病人的受伤情况。对于统计报告,需要将此文本数据映射到预定义的代码。该领域的现有研究使用Naïve贝叶斯概率方法来构建映射分类器。在本文中,我们的重点是为分类方法的选择提供指导。我们构建了许多属于不同分类族的分类器,如决策树、概率、神经网络以及基于实例、基于集成和基于核的线性分类器。进行广泛的预处理以确保数据的质量,从而确保质量分类结果。在伤害描述中带有空条目的记录将被删除。拼写错误的纠正过程是通过查找和替换拼写错误的单词与发音相近的单词来完成的。有意义的短语被识别和保留,而不是删除短语的一部分作为停止词。在许多形式的条目中出现的缩写都是手动识别的,并且只使用一种形式的缩写。聚类用于区分非频繁术语和频繁术语。这个过程将文本特征的数量从28000个显著减少到5000个。医学叙事文本损伤数据集是由许多短文件组成的。数据具有高维和稀疏的特征,即很少有特征是不相关的,但特征之间是相互关联的。因此,奇异值分解(SVD)和非负矩阵分解(NNMF)等矩阵分解技术被用于将处理后的特征空间映射到更低维的特征空间。用这些简化的特征空间构建分类器。在实验中,进行了一组测试,以反映哪种分类方法最适合医学文本分类。基于支持向量机的非负矩阵分解方法可以达到93%的准确率,高于所有经过测试的传统分类器。我们还发现,TF/IDF加权在长文本分类中效果很好,但在短文档分类中却不如二元加权。另一个发现是,在与医学专家协商后,应该删除排名前n的术语,因为它会影响分类效果。
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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