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

Dingcheng Li; Peini Liu; Ming Huang; Yu Gu; Yue Zhang; Xiaodi Li; Daniel Dean; Xiaoxi Liu; Jingmin Xu; Hui Lei; Yaoping Ruan

doi:10.1109/BIBM.2017.8217680

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

Dingcheng Li, Peini Liu, Ming Huang, Yu Gu, Yue Zhang, Xiaodi Li, Daniel Dean, Xiaoxi Liu, Jingmin Xu, Hui Lei, Yaoping Ruan

Artificial Intelligence and Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Editors	Illhoi Yoo, Jane Huiru Zheng, Yang Gong, Xiaohua Tony Hu, Chi-Ren Shyu, Yana Bromberg, Jean Gao, Dmitry Korkin
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	386-391
Number of pages	6
ISBN (Electronic)	9781509030491
DOIs	https://doi.org/10.1109/BIBM.2017.8217680
State	Published - Dec 15 2017
Event	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 - Kansas City, United States Duration: Nov 13 2017 → Nov 16 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Volume	2017-January

Other

Other	2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Country/Territory	United States
City	Kansas City
Period	11/13/17 → 11/16/17

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics

Access to Document

10.1109/BIBM.2017.8217680

Cite this

Li, D., Liu, P., Huang, M., Gu, Y., Zhang, Y., Li, X., Dean, D., Liu, X., Xu, J., Lei, H., & Ruan, Y. (2017). Mapping client messages to a unified data model with mixture feature embedding convolutional neural network. In I. Yoo, J. H. Zheng, Y. Gong, X. T. Hu, C.-R. Shyu, Y. Bromberg, J. Gao, & D. Korkin (Eds.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017 (pp. 386-391). (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2017.8217680

Mapping client messages to a unified data model with mixture feature embedding convolutional neural network. / Li, Dingcheng; Liu, Peini; Huang, Ming et al.
Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. ed. / Illhoi Yoo; Jane Huiru Zheng; Yang Gong; Xiaohua Tony Hu; Chi-Ren Shyu; Yana Bromberg; Jean Gao; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. p. 386-391 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017; Vol. 2017-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, D, Liu, P, Huang, M, Gu, Y, Zhang, Y, Li, X, Dean, D, Liu, X, Xu, J, Lei, H & Ruan, Y 2017, Mapping client messages to a unified data model with mixture feature embedding convolutional neural network. in I Yoo, JH Zheng, Y Gong, XT Hu, C-R Shyu, Y Bromberg, J Gao & D Korkin (eds), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 386-391, 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017, Kansas City, United States, 11/13/17. https://doi.org/10.1109/BIBM.2017.8217680

Li D, Liu P, Huang M, Gu Y, Zhang Y, Li X et al. Mapping client messages to a unified data model with mixture feature embedding convolutional neural network. In Yoo I, Zheng JH, Gong Y, Hu XT, Shyu CR, Bromberg Y, Gao J, Korkin D, editors, Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 386-391. (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017). doi: 10.1109/BIBM.2017.8217680

Li, Dingcheng ; Liu, Peini ; Huang, Ming et al. / Mapping client messages to a unified data model with mixture feature embedding convolutional neural network. Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. editor / Illhoi Yoo ; Jane Huiru Zheng ; Yang Gong ; Xiaohua Tony Hu ; Chi-Ren Shyu ; Yana Bromberg ; Jean Gao ; Dmitry Korkin. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 386-391 (Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017).

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abstract = "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.",

author = "Dingcheng Li and Peini Liu and Ming Huang and Yu Gu and Yue Zhang and Xiaodi Li and Daniel Dean and Xiaoxi Liu and Jingmin Xu and Hui Lei and Yaoping Ruan",

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doi = "10.1109/BIBM.2017.8217680",

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AU - Li, Dingcheng

AU - Liu, Peini

AU - Huang, Ming

AU - Gu, Yu

AU - Zhang, Yue

AU - Li, Xiaodi

AU - Dean, Daniel

AU - Liu, Xiaoxi

AU - Xu, Jingmin

AU - Lei, Hui

AU - Ruan, Yaoping

PY - 2017/12/15

Y1 - 2017/12/15

N2 - 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.

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

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