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Active Learning for Conversational Interfaces in Healthcare Applications

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Artificial Intelligence in Health (AIH 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11326))

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Abstract

In automated health services based on text and voice interfaces, there is a need to be able to understand what the user is talking about, and what is the attitude of the user towards a subject. Typical machine learning methods for text analysis require a lot of annotated data for the training. This is often a problem in addressing specific and possibly very personal health care needs. In this paper, we propose an active learning algorithm for the training of a text classifier for a conversational therapy application in the area of health behavior change. A new active learning algorithm, Query by Embedded Committee (QBEC), is proposed in the paper. The methods are particularly suitable for the text classification task in a dynamic environment and give a good performance with realistic test data.

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Notes

  1. 1.

    The ethical and legal approval of the data collection was granted, and handled according to, by the Internal Committee for Biomedical Experiments (ICBE) of Philips.

References

  1. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)

  2. Bickmore, T., Giorgino, T.: Health dialog systems for patients and consumers. J. Biomed. Inform. 39(5), 556–571 (2006). https://doi.org/10.1016/j.jbi.2005.12.004

    Article  Google Scholar 

  3. Cohn, D.A., Ghahramani, Z., Jordan, M.I.: Active learning with statistical models. J. Artif. Int. Res. 4(1), 129–145 (1996). http://dl.acm.org/citation.cfm?id=1622737.1622744

    Article  Google Scholar 

  4. Dasgupta, S.: Coarse sample complexity bounds for active learning. In: NIPS 2005, pp. 235–242. MIT Press, Cambridge (2005). http://dl.acm.org/citation.cfm?id=2976248.2976278

  5. Delaunay, B.: Sur la sphere vide. a la memoire de georges voronoi. Bulletin del’Academie des Sciences del’URSS (6), 793–800 (1934)

    Google Scholar 

  6. Fawzi, A., Moosavi-Dezfooli, S.M., Frossard, P.: The robustness of deep networks: a geometrical perspective. IEEE Sig. Process. Mag. 34(6), 50–62 (2017). https://doi.org/10.1109/MSP.2017.2740965

    Article  Google Scholar 

  7. Freund, Y., Seung, H.S., Shamir, E., Tishby, N.: Selective sampling using the query by committee algorithm. Mach. Learn. 12(28), 133–168 (1997)

    Article  Google Scholar 

  8. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  9. Hofmann, S.G., Asnaani, A., Vonk, I.J., Sawyer, A.T., Fang, A.: The efficacy of cognitive behavioral therapy: a review of meta-analyses. Cogn. Ther. Res. 36(5), 427–440 (2012). https://doi.org/10.1007/s10608-012-9476-1, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3584580/

    Article  Google Scholar 

  10. Hoi, S.C.H., Jin, R., Lyu, M.R.: Batch mode active learning with applications to text categorization and image retrieval. IEEE Trans. Knowl. Data Eng. 21(9), 1233–1248 (2009). https://doi.org/10.1109/TKDE.2009.60

    Article  Google Scholar 

  11. Liu, T., Moore, A.W., Gray, A., Yang, K.: An investigation of practical approximate nearest neighbor algorithms. In: Saul, L.K., Weiss, Y., Bottou, L. (eds.) Advances in Neural Information Processing Systems, pp. 825–832. MIT Press, Cambridge (2015)

    Google Scholar 

  12. McCallum, A., Nigam, K.: Employing EM and pool-based active learning for text classification. In: ICML 1998, pp. 350–358. Morgan Kaufmann Publishers Inc., San Francisco (1998). http://dl.acm.org/citation.cfm?id=645527.757765

  13. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)

  14. Miller, W.R., Rose, G.S.: Toward a theory of motivational interviewing. Am. Psychol. 64(6), 527–537 (2009). https://doi.org/10.1037/a0016830, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2759607/

    Article  Google Scholar 

  15. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014)

    Google Scholar 

  16. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2, 45–66 (2002). https://doi.org/10.1162/153244302760185243

    Article  MATH  Google Scholar 

  17. Wang, L., Hu, X., Yuan, B., Lu, J.: Active learning via query synthesis and nearest neighbour search. Neurocomputing 147(Suppl. C), 426–434 (2015). https://doi.org/10.1016/j.neucom.2014.06.042, http://www.sciencedirect.com/science/article/pii/S0925231214008145

    Article  Google Scholar 

  18. Ying, S., Xu, G., Li, C., Mao, Z.: Point cluster analysis using a 3D Voronoi diagram with applications in point cloud segmentation. Int. J. Geo-Inf. (ISPRS) 4(3), 1480–1499 (2015)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Philips Research, Eindhoven, The Netherlands

    Aki Härmä

  2. Philips Research, Moscow, Russia

    Andrey Polyakov

  3. Sberbank, Moscow, Russia

    Ekaterina Artemova

Authors
  1. Aki Härmä
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  2. Andrey Polyakov
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  3. Ekaterina Artemova
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Corresponding author

Correspondence to Aki Härmä .

Editor information

Editors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Fernando Koch

  2. IIIA-CSIC and Autonomous University of Barcelona, Barcelona, Spain

    Andrew Koster

  3. Universitat Rovira i Virgili, Tarragona, Spain

    David Riaño

  4. Università di Bologna, Cesena, Italy

    Sara Montagna

  5. HES-SO Valais Wallis, Sierre, Switzerland

    Michael Schumacher

  6. VU University Amsterdam, Amsterdam, The Netherlands

    Annette ten Teije

  7. Karolinska Institute, Stockholm, Sweden

    Christian Guttmann

  8. University of Ulm, Ulm, Germany

    Manfred Reichert

  9. State University of New York, Oswego, NY, USA

    Isabelle Bichindaritz

  10. Imperial College London, London, UK

    Pau Herrero

  11. University of Erlangen and Nuremberg, Erlangen, Germany

    Richard Lenz

  12. University of Girona, Girona, Spain

    Beatriz López

  13. Ohio University, Athens, OH, USA

    Cindy Marling

  14. Oxford Brookes University, Oxford, UK

    Clare Martin

  15. Università del Piemonte Orientale, Alessandria, Italy

    Stefania Montani

  16. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, UK

    Nirmalie Wiratunga

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Härmä, A., Polyakov, A., Artemova, E. (2019). Active Learning for Conversational Interfaces in Healthcare Applications. In: Koch, F., et al. Artificial Intelligence in Health. AIH 2018. Lecture Notes in Computer Science(), vol 11326. Springer, Cham. https://doi.org/10.1007/978-3-030-12738-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-12738-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12737-4

  • Online ISBN: 978-3-030-12738-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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