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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.

References

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

  2. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, pp. 2787–2795 (2013)

    Google Scholar 

  3. 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 

  4. 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 

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

    Google Scholar 

  6. Cui, Y., et al.: Pre-training with whole word masking for Chinese BERT. arXiv preprint arXiv:1906.08101 (2019)

  7. 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, Minneapolis, Minnesota, vol. 1, pp. 4171–4186. Association for Computational Linguistics (2019)

    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. Chin. 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, Melbourne, Australia, vol. 2, pp. 332–338. Association for Computational Linguistics (2018)

    Google Scholar 

  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, New Orleans, Louisiana, vol. 1, pp. 2227–2237. Association for Computational Linguistics (2018)

    Google Scholar 

  15. Qu, C., Yang, L., Qiu, M., Croft, W.B., Zhang, Y., Iyyer, M.: BERT with history answer embedding for conversational question answering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1133–1136 (2019)

    Google Scholar 

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

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

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

  19. 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 

  20. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  21. 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 

  22. Yang, H.l., Yang, Z.: Effect of older pregnancy on maternal and fetal outcomes. Chin. J. Obstetric Emerg. (Electron. Edn.) 5(3), 129–135 (2016)

    Google Scholar 

  23. Yang, P., Sun, X., Li, W., Ma, S., Wu, W., Wang, H.: SGM: sequence generation model for multi-label classification, pp. 3915–3926 (2018)

    Google Scholar 

  24. 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 

  25. 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 

  26. 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)

    Google Scholar 

  27. 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 

  28. 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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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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