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A multiclass classification method based on deep learning for named entity recognition in electronic medical records

机译：一种基于深度学习的多牌分类方法，用于电子医疗记录中的指定实体识别

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Research of named entity recognition (NER) on electrical medical records (EMRs) focuses on verifying whether methods to NER in traditional texts are effective for that in EMRs, and there is no model proposed for enhancing performance of NER via deep learning from the perspective of multiclass classification. In this paper, we annotate a real EMR corpus to accomplish the model training and evaluation. And, then, we present a Convolutional Neural Network (CNN) based multiclass classification method for mining named entities from EMRs. The method consists of two phases. In the phase 1, EMRs are pre-processed for representing samples with word embedding. In the phase 2, the method is built by segmenting training data into many subsets and training a CNN binary classification model on each of subset. Experimental results showed the effectiveness of our method.
机译：关于电气医疗记录（EMRS）的命名实体识别（NER）的研究侧重于验证传统文本中NER的方法是否有效，在EMRS中是有效的，并且没有建议通过深度学习来提高NER的性能。多包子分类。在本文中，我们注释了一个真正的EMR语料库来完成模型培训和评估。然后，我们提出了一种基于卷积神经网络（CNN）的多款类别分类方法，用于从EMRS挖掘命名实体。该方法包括两个阶段。在阶段1中，预处理EMRS以表示具有单词嵌入的样本。在阶段2中，通过将训练数据分段为许多子集来构建该方法，并在每个子集上训练CNN二进制分类模型。实验结果表明了我们方法的有效性。

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