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Knowledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph

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Chinese Computational Linguistics (CCL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12522))

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Abstract

The obstetric Electronic Medical Record (EMR) contains a large amount of medical data and health information. It plays 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-Enabled Diagnosis Assistant (KEDA) model for the obstetric diagnosis assistant. We utilize the numerical information in EMRs and the external knowledge from Chinese Obstetric Knowledge Graph (COKG) to enhance the text representation of EMRs. Specifically, the bidirectional maximum matching method and similarity-based approach are used to obtain the entities set contained in EMRs and linked to the COKG. The final knowledge representation is obtained by a weight-based disease prediction algorithm, and it is fused with the text representation through a linear weighting method. Experiment results show that our approach can bring about +3.53 F1 score improvements upon the strong BERT baseline in the diagnosis assistant task.

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 Co-sponsored 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).

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Notes

  1. 1.

    http://47.106.35.172:8088/.

  2. 2.

    http://www5.zzu.edu.cn/nlp/info/1015/1865.htm

References

  1. Baker, S., Korhonen, A.: Initializing neural networks for hierarchical multi-label text classification. In: BioNLP 2017, pp. 307–315. Association for Computational Linguistics, Vancouver, Canada, August 2017. https://doi.org/10.18653/v1/W17-2339, https://www.aclweb.org/anthology/W17-2339

  2. Chen, G., Ye, D., Xing, Z., Chen, J., Cambria, E.: Ensemble application of convolutional and recurrent neural networks for multi-label text categorization. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 2377–2383. IEEE (2017)

    Google Scholar 

  3. Chen, J., Hu, Y., Liu, J., Xiao, Y., Jiang, H.: Deep short text classification with knowledge powered attention. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp 6252–6259 (2019)

    Google Scholar 

  4. China’s Ministry of Health: Basic specification of electronic medical records (trial). Technical Report 3 (2010)

    Google Scholar 

  5. Cui, Y., et al.: Pre-training with whole word masking for chinese bert. arXiv preprint arXiv:1906.08101 (2019)

  6. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https://doi.org/10.18653/v1/N19-1423, https://www.aclweb.org/anthology/N19-1423

  7. Gai, R.L., Gao, F., Duan, L.M., Sun, X.H., Li, H.Z.: Bidirectional maximal matching word segmentation algorithm with rules. In: Advanced Materials Research, vol. 926, pp. 3368–3372. Trans Tech Publ. (2014)

    Google Scholar 

  8. Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: Spanbert: improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Linguist. 8, 64–77 (2020)

    Article  Google Scholar 

  9. Kurata, G., Xiang, B., Zhou, B.: Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 521–526 (2016)

    Google Scholar 

  10. Li, M., Clinton, G., Miao, Y., Gao, F.: Short text classification via knowledge powered attention with similarity matrix based CNN. arXiv preprint arXiv:2002.03350 (2020)

  11. Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)

  12. Ma, H., Zhang, K., Zhao, Y.: Study on obstetric multi-label assisted diagnosis based on feature fusion. J. Chinese Inf. Process. 32(5), 128–136 (2018)

    Google Scholar 

  13. Ma, S., Sun, X., Wang, Y., Lin, J.: Bag-of-words as target for neural machine translation. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pp. 332–338. Association for Computational Linguistics, Melbourne, Australia, July 2018. https://doi.org/10.18653/v1/P18-2053, https://www.aclweb.org/anthology/P18-2053

  14. Peters, M., et al.: Deep contextualized word representations. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp. 2227–2237. Association for Computational Linguistics, New Orleans, Louisiana, June 2018. https://doi.org/10.18653/v1/N18-1202, https://www.aclweb.org/anthology/N18-1202

  15. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding with unsupervised learning. Technical report, OpenAI (2018)

    Google Scholar 

  16. Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)

    Article  MathSciNet  Google Scholar 

  17. Sun, Y., et al.: Ernie: enhanced representation through knowledge integration. arXiv preprint arXiv:1904.09223 (2019)

  18. Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2010)

    Article  Google Scholar 

  19. Vaswani, A., et al.: Attention is all you need. In: Advances in neural information processing systems, pp. 5998–6008 (2017)

    Google Scholar 

  20. Yang, A., et al.: Enhancing pre-trained language representations with rich knowledge for machine reading comprehension. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2346–2357 (2019)

    Google Scholar 

  21. Yang, H.l., Yang, Z.: Effect of older pregnancy on maternal and fetal outcomes. Chinese J. Obstetric Emergency (Electr. Edn) 5(3), 129–135 (2016)

    Google Scholar 

  22. Yang, P., et al.: SGM: sequence generation model for multi-label classification, pp. 3915–3926 (2018)

    Google Scholar 

  23. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R.R., Le, Q.V.: Xlnet: generalized autoregressive pretraining for language understanding. In: Advances in Neural Information Processing Systems, pp. 5754–5764 (2019)

    Google Scholar 

  24. Zhang, K., Liu, C., Duan, X., Zhou, L., Zhao, Y., Zan, H.: Bert with enhanced layer for assistant diagnosis based on chinese obstetric EMRS. In: 2019 International Conference on Asian Language Processing (IALP), pp. 384–389. IEEE (2019)

    Google Scholar 

  25. Zhang, K., Ma, H., Zhao, Y., Zan, H., Zhuang, L.: The comparative experimental study of multilabel classification for diagnosis assistant based on Chinese obstetric EMRS. J. Healthcare Eng. 2018 (2018)

    Google Scholar 

  26. Zhang, M.L., Zhou, Z.H.: Multilabel neural networks with applications to functional genomics and text categorization. IEEE Trans. Knowl. Data Eng. 18(10), 1338–1351 (2006)

    Article  Google Scholar 

  27. Zhang, M.L., Zhou, Z.H.: Ml-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)

    Article  Google Scholar 

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Authors and Affiliations

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

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

  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. Qi Xie
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  5. Hongying Zan
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Corresponding author

Correspondence to Xu Zhao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Peking University, Beijing, China

    Sujian Li

  3. Westlake University, Hangzhou, China

    Yue Zhang

  4. Tsinghua University, Beijing, China

    Yang Liu

  5. Chinese Academy of Sciences, Beijing, China

    Shizhu He

  6. Beijing Language and Culture University, Beijing, China

    Gaoqi Rao

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Zhang, K., Zhao, X., Zhuang, L., Xie, Q., Zan, H. (2020). Knowledge-Enabled Diagnosis Assistant Based on Obstetric EMRs and Knowledge Graph. In: Sun, M., Li, S., Zhang, Y., Liu, Y., He, S., Rao, G. (eds) Chinese Computational Linguistics. CCL 2020. Lecture Notes in Computer Science(), vol 12522. Springer, Cham. https://doi.org/10.1007/978-3-030-63031-7_32

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  • DOI: https://doi.org/10.1007/978-3-030-63031-7_32

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-63031-7

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