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Medical Forum Question Classification Using Deep Learning

  • Raksha Jalan
  • Manish GuptaEmail author
  • Vasudeva Varma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)

Abstract

With the rapid increase in the number as well as quality of online medical forums, patients are increasingly using the Internet for health information and support. Online health forums play an important role in addressing consumers health information needs. However, given the large number of queries, and limited number of experts, a significant fraction of the questions remains unanswered. Automatic question classifiers can overcome this issue by directing questions to specific experts according to their topic preferences to get quick and better responses.

In this paper, we aim to classify health forum questions where classes of questions mainly focus on capturing user intentions. We strongly believe that a good estimate of user intentions will help direct their questions to the best responders. We propose a novel approach of combining medical domain based features with deep learning models for question classification task. To further improve performance of the data-hungry deep learning models, we resort to weak supervision strategies. We propose a new variant of the existing self-training method called “Self-Training with Lookups” for weak supervision. Our results demonstrate that combining features generated from biomedical entities along with other language representation features for deep learning networks can lead to substantial improvement in modeling user generated health content. Weak supervision further enhances the accuracy. The proposed model outperforms the state-of-the-art method on a benchmark dataset of 11000 questions with a margin of 3.13%.

Keywords

Medical question classification Deep learning models Weak supervision 

References

  1. 1.
    Aronson, A.R.: Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program. In: AMIA, p. 17 (2001)Google Scholar
  2. 2.
    Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning. IEEE Trans. Neural Netw. 20, 542–542 (2009)CrossRefGoogle Scholar
  3. 3.
    Christopher, D.M., Prabhakar, R., Hinrich, S.: An Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008). 151, 177zbMATHGoogle Scholar
  4. 4.
    Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-45014-9_1 CrossRefGoogle Scholar
  5. 5.
    Guo, H., Na, X., Hou, L., Li, J.: Classifying Chinese questions related to health care posted by consumers via the internet. J. Med. Internet Res. 19(6), e220 (2017)CrossRefGoogle Scholar
  6. 6.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)CrossRefGoogle Scholar
  7. 7.
    Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: ICML (2014)Google Scholar
  8. 8.
    Liu, F., Antieau, L.D., Yu, H.: Toward automated consumer question answering: automatically separating consumer questions from professional questions in the healthcare domain. J. Bio. Info. 44, 1032–1038 (2011)CrossRefGoogle Scholar
  9. 9.
    Mikolov, T., Karafiát, M., Burget, L., Cernockỳ, J., Khudanpur, S.: Recurrent neural network based language model. In: Interspeech (2010)Google Scholar
  10. 10.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS (2013)Google Scholar
  11. 11.
    Moen, S., Ananiadou, T.S.S.: Distributional Semantics Resources for Biomedical Text Processing (2013)Google Scholar
  12. 12.
    Mrabet, Y., Kilicoglu, H., Roberts, K., Demner-Fushman, D.: Combining open-domain and biomedical knowledge for topic recognition in consumer health questions. In: AMIA, vol. 2016, p. 914 (2016)Google Scholar
  13. 13.
    Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)Google Scholar
  14. 14.
    Roberts, K., Rodriguez, L., Shooshan, S.E., Demner-Fushman, D.: Resource classification for medical questions. In: AMIA (2016)Google Scholar
  15. 15.
    Ruder, S., Ghaffari, P., Breslin, J.G.: A hierarchical model of reviews for aspect-based sentiment analysis. arXiv preprint arXiv:1609.02745 (2016)
  16. 16.
    Socher, R., Bengio, Y., Manning, C.D.: Deep learning for NLP (without magic). In: Tutorial Abstracts of ACL 2012, pp. 5–5. Association for Computational Linguistics (2012)Google Scholar
  17. 17.
    Tan, M., Santos, C.D., Xiang, B., Zhou, B.: LSTM-based deep learning models for non-factoid answer selection. arXiv preprint arXiv:1511.04108 (2015)
  18. 18.
    Verma, J., Kwon, B.C., Cheng, Y., Ghosh, S., Ng, K.: Classification of healthcare forum messages. In: ICHI (2016)Google Scholar
  19. 19.
    Yang, Z., Yang, D., Dyer, C., He, X., Smola, A.J., Hovy, E.H.: Hierarchical attention networks for document classification. In: HLT-NAACL, pp. 1480–1489 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.International Institute of Information Technology-HyderabadHyderabadIndia

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