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Obstetric Diagnosis Assistant via Knowledge Powered Attention and Information-Enhanced Strategy

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Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence (CCKS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1356))

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Abstract

The obstetric Electronic Medical Records (EMRs) contain a large amount of medical data and health information. The obstetric EMRs play a vital role in improving the quality of the diagnosis assistant service. In this paper, we treat the diagnosis assistant as a multi-label classification task and propose a Knowledge powered Attention and Information-Enhanced (KAIE) model for the obstetric diagnosis assistant. In order to make most of the information in EMRs, we propose to utilize the numerical information and chief complaint information to enhance the text representation. In addition to the use of information in EMRs, we integrate external knowledge from the COKG medical knowledge graph into the model. Specifically, we propose a multi-way attention mechanism for the generation of knowledge-aware representations based on text representations. Experiment results show that our approach is able to bring about +1.37 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.

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Notes

  1. 1.

    http://47.106.35.172:8088/.

  2. 2.

    https://github.com/google-research/bert.

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Acknowledgements

This work has been supported by the National Key Research and Development Project (Grant No. 2017YFB1002101), Major Program of National Social Science Foundation of China (Grant No. 17ZDA138), China Postdoctoral Science Foundation (Grant No. 2019TQ0286), Science and Technique Program of Henan Province (Grant No. 192102210260), Medical Science and Technique Program Cosponsored by Henan Province and Ministry (Grant No. SB201901021), Key Scientific Research Program of Higher Education of Henan Province (Grant No. 19A520003, 20A520038), the MOE Layout Foundation of Humanities and Social Sciences (Grant No. 20YJA740033), and the Henan Social Science Planning Project (Grant No. 2019BYY016).

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhengzhou University, Zhengzhou, China

    Kunli Zhang, Xu Zhao, Lei Zhuang, Hongying Zan & Qi Xie

  2. Peng Cheng Laboratory, Shenzhen, China

    Kunli Zhang, Xu Zhao & Hongying Zan

Authors
  1. Kunli Zhang
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  2. Xu Zhao
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  3. Lei Zhuang
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  4. Hongying Zan
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  5. Qi Xie
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Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Huajun Chen

  2. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Kang Liu

  3. University of California, Los Angeles, CA, USA

    Yizhou Sun

  4. Shanxi University, Taiyuan, China

    Suge Wang

  5. Tsinghua University, Beijing, China

    Lei Hou

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Zhang, K., Zhao, X., Zhuang, L., Zan, H., Xie, Q. (2021). Obstetric Diagnosis Assistant via Knowledge Powered Attention and Information-Enhanced Strategy. In: Chen, H., Liu, K., Sun, Y., Wang, S., Hou, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Graph and Cognitive Intelligence. CCKS 2020. Communications in Computer and Information Science, vol 1356. Springer, Singapore. https://doi.org/10.1007/978-981-16-1964-9_22

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  • DOI: https://doi.org/10.1007/978-981-16-1964-9_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-1963-2

  • Online ISBN: 978-981-16-1964-9

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