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Domain Supervised Deep Learning Framework for Detecting Chinese Diabetes-Related Topics

  • Xinhuan ChenEmail author
  • Yong Zhang
  • Kangzhi Zhao
  • Qingcheng Hu
  • Chunxiao Xing
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10828)

Abstract

As millions of people are diagnosed with diabetes every year in China, many diabetes-related websites in Chinese provide news and articles. However, most of the online articles are uncategorized or lack a clear or unified topic, users often cannot find their topics of interest effectively and efficiently. The problem of health text classification on Chinese websites cannot be easily addressed by applying existing approaches, which have been used for English documents, in a straightforward manner. To address this problem and meet users’ demand for diabetes-related information needs, we propose a Chinese domain lexicon, adopt some professional diabetes topic explanations as domain knowledge and incorporate them into deep learning approach to form our topic classification framework. Our experiments using real datasets showed that the framework significantly achieved a higher effectiveness and accuracy in categorizing diabetes-related topics than most of the state-of-the-art benchmark approaches. Our experimental analysis also revealed that some health websites provided some incorrect or misleading category information.

Keywords

Domain knowledge Stacked Denoising Autoencoders Healthcare Chinese 

Notes

Acknowledgments

This work was supported by NSFC (91646202), NSSFC (15CTQ028), the National High-tech R&D Program of China (SS2015AA020102), Research/Project 2017YB142 supported by Ministry of Education of The People’s Republic of China, the 1000-Talent program and Tsinghua University Initiative Scientific Research Program.

References

  1. 1.
    Adeva, J.G., Atxa, J.P., Carrillo, M.U., Zengotitabengoa, E.A.: Automatic text classification to support systematic reviews in medicine. Expert Syst. Appl. 41(4), 1498–1508 (2014)CrossRefGoogle Scholar
  2. 2.
    Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucleic Acids Res. 32(Suppl. 1), 267–270 (2004)CrossRefGoogle Scholar
  3. 3.
    Bollegala, D., Mu, T., Goulermas, J.Y.: Cross-domain sentiment classification using sentiment sensitive embeddings. TKDE 28(2), 398–410 (2016)Google Scholar
  4. 4.
    Charalampous, K., Gasteratos, A.: A tensor-based deep learning framework. Image Vis. Comput. 32(11), 916–929 (2014)CrossRefGoogle Scholar
  5. 5.
    Cheeseman, P., Kelly, J., Self, M., Stutz, J., Taylor, W., Freeman, D.: Autoclass: a Bayesian classification system. In: Readings in Knowledge Acquisition and Learning, pp. 431–441 (1993)Google Scholar
  6. 6.
    Chen, X., Zhang, Y., Xing, C., Liu, X., Chen, H.: Diabetes-related topic detection in chinese health websites using deep learning. In: Zheng, X., Zeng, D., Chen, H., Zhang, Y., Xing, C., Neill, D.B. (eds.) ICSH 2014. LNCS, vol. 8549, pp. 13–24. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08416-9_2CrossRefGoogle Scholar
  7. 7.
    Hinton, G., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
  10. 10.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)Google Scholar
  11. 11.
    Liu, B., Huang, M., Sun, J., Zhu, X.: Incorporating domain and sentiment supervision in representation learning for domain adaptation. In: IJCAI, pp. 1277–1283 (2015)Google Scholar
  12. 12.
    Liu, W., Sweeney, H.J., Chung, B., Glance, D.G.: Constructing consumer-oriented medical terminology from the web a supervised classifier ensemble approach. In: Pham, D.-N., Park, S.-B. (eds.) PRICAI 2014. LNCS (LNAI), vol. 8862, pp. 770–781. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-13560-1_61CrossRefGoogle Scholar
  13. 13.
    Liu, X., Chen, H.: AZDrugMiner: an information extraction system for mining patient-reported adverse drug events in online patient forums. In: Zeng, D., Yang, C.C., Tseng, V.S., Xing, C., Chen, H., Wang, F.-Y., Zheng, X. (eds.) ICSH 2013. LNCS, vol. 8040, pp. 134–150. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-39844-5_16CrossRefGoogle Scholar
  14. 14.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)Google Scholar
  15. 15.
    Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 1, 11 (2017). bbx044Google Scholar
  16. 16.
    Nie, L., Wang, M., Zhang, L., Yan, S., Zhang, B., Chua, T.S.: Disease inference from health-related questions via sparse deep learning. TKDE 27(8), 2107–2119 (2015)Google Scholar
  17. 17.
    Nolle, T., Seeliger, A., Mühlhäuser, M.: Unsupervised anomaly detection in noisy business process event logs using denoising autoencoders. In: Calders, T., Ceci, M., Malerba, D. (eds.) DS 2016. LNCS (LNAI), vol. 9956, pp. 442–456. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46307-0_28CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  20. 20.
    Rosenblatt, F.: The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory (1957)Google Scholar
  21. 21.
    Salakhutdinov, R., Hinton, G.: Semantic hashing. Int. J. Approx. Reason. 50(7), 969–978 (2009)CrossRefGoogle Scholar
  22. 22.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval (1986)Google Scholar
  23. 23.
    Sarikaya, R., Hinton, G.E., Deoras, A.: Application of deep belief networks for natural language understanding. IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP) 22(4), 778–784 (2014)CrossRefGoogle Scholar
  24. 24.
    Schmitt, B.H., Pan, Y., Tavassoli, N.T.: Language and consumer memory: the impact of linguistic differences between Chinese and English. J. Consum. Res. 21, 419–431 (1994)CrossRefGoogle Scholar
  25. 25.
    Sibunruang, C., Polpinij, J.: Ontology-based text classification for filtering cholangiocarcinoma documents from PubMed. In: Ślȩzak, D., Tan, A.-H., Peters, J.F., Schwabe, L. (eds.) BIH 2014. LNCS (LNAI), vol. 8609, pp. 266–277. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-09891-3_25CrossRefGoogle Scholar
  26. 26.
    Simon, G.J., Caraballo, P.J., Therneau, T.M., Cha, S.S., Castro, M.R., Li, P.W.: Extending association rule summarization techniques to assess risk of diabetes mellitus. TKDE 27(1), 130–141 (2015)Google Scholar
  27. 27.
    Sinha, A.P., Zhao, H.: Incorporating domain knowledge into data mining classifiers: an application in indirect lending. Decis. Support Syst. 46(1), 287–299 (2008)CrossRefGoogle Scholar
  28. 28.
    Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.A.: Extracting and composing robust features with denoising autoencoders. In: ICML, pp. 1096–1103. ACM (2008)Google Scholar
  29. 29.
    Wang, H., Shi, X., Yeung, D.Y.: Relational stacked denoising autoencoder for tag recommendation. In: AAAI, pp. 3052–3058 (2015)Google Scholar
  30. 30.
    Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: IJCAI, pp. 2915–2921 (2017)Google Scholar
  31. 31.
    Wu, Z., Jiang, Y.G., Wang, J., Pu, J., Xue, X.: Exploring inter-feature and inter-class relationships with deep neural networks for video classification. In: MM, pp. 167–176. ACM (2014)Google Scholar
  32. 32.
    Xu, W., Sun, H., Deng, C., Tan, Y.: Variational autoencoder for semi-supervised text classification. In: AAAI, pp. 3358–3364 (2017)Google Scholar
  33. 33.
    Yang, H., Kundakcioglu, E., Li, J., Wu, T., Mitchell, J.R., Hara, A.K., Pavlicek, W., Hu, L.S., Silva, A.C., Zwart, C.M., et al.: Healthcare intelligence: turning data into knowledge. IEEE Intell. Syst. 29(3), 54–68 (2014)CrossRefGoogle Scholar
  34. 34.
    Yin, W., Schütze, H.: Deep learning embeddings for discontinuous linguistic units. arXiv preprint arXiv:1312.5129 (2013)
  35. 35.
    Yin, X.: Diabetology. Shanghai Scientific and Technical Publishers (2003)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Xinhuan Chen
    • 1
    Email author
  • Yong Zhang
    • 1
  • Kangzhi Zhao
    • 1
  • Qingcheng Hu
    • 1
  • Chunxiao Xing
    • 1
  1. 1.Research Institute of Information Technology, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Institute of Internet IndustryTsinghua UniversityBeijingChina

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