Mapping client messages to a unified data model with mixture feature embedding convolutional neural network
2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)(2017)
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.
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关键词
unified data model,data mapping,health institutes,data exchanges,deep learning method,MfeCNN,multiple classification problem,multimodal features,multiview embedding,data standards,semantic space,feature embeddings,client message mapping,ixture feature embedding convolutional neural network,fully-connected soft-max layer
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