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Mapping client messages to a unified data model with mixture feature embedding convolutional neural network

机译：使用混合特征嵌入卷积神经网络将客户端消息映射到统一数据模型

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摘要

Data mapping among different data standards in health institutes is often a necessity when data exchanges occur among different institutes. However, no matter rule-based approaches or traditional machine learning methods, none of these methods have achieved satisfactory results yet. In this work, we propose a deep learning method, mixture feature embedding convolutional neural network (MfeCNN), to convert the data mapping to a multiple classification problem. Multi-modal features were extracted from different semantic space with a medical NLP package and powerful feature embeddings were generated by MfeCNN. Classes as many as ten were classified simultaneously by a fully-connected soft-max layer based on multi-view embedding. Experimental results show that our proposed MfeCNN achieved best results than traditional state-of-the-art machine learning models and also much better results than the convolutional neural network of only using bag-of-words as inputs.
机译：当不同机构之间进行数据交换时，卫生机构中不同数据标准之间的数据映射通常是必要的。但是，无论是基于规则的方法还是传统的机器学习方法，这些方法都没有取得令人满意的结果。在这项工作中，我们提出了一种深度学习方法，即混合特征嵌入卷积神经网络（MfeCNN），以将数据映射转换为多重分类问题。使用医学NLP包从不同的语义空间中提取多模式特征，并通过MfeCNN生成强大的特征嵌入。基于多视图嵌入的完全连接的soft-max层可同时分类多达十个类。实验结果表明，与传统的最新机器学习模型相比，我们提出的MfeCNN取得了最佳结果，并且比仅使用词袋作为输入的卷积神经网络取得了更好的结果。

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