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Generation of Non-compliant Behaviour in Virtual Medical Narratives

  • Alan Lindsay
  • Fred Charles
  • Jonathon Read
  • Julie Porteous
  • Marc Cavazza
  • Gersende Georg
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9238)

Abstract

Patient education documents increasingly take the form of Patient Guidelines, which share many of the properties of clinical guidelines in terms of knowledge content and the description of clinical protocols. They however differ in one specific aspect, which is that some recommendations for patient behaviour may be violated, and that no explicit representation of undesired behaviour is embedded in the guidelines themselves. In this paper, we take as a starting point the plan-based representation of clinical guidelines, which has been promoted by several authors, and introduce a method to automatically derive the set of “opposite actions” that constitute violations of recommended patient behaviours. These additional alternative actions are generated automatically as PDDL operators complementing the description of the guideline. As an application, using a patient guideline on bariatric surgery, we also present examples of how these actions can be used to visualise undesirable patient behaviour in a 3D serious game, featuring virtual agents representing the patient and healthcare professionals.

Keywords

Bariatric Surgery Virtual Environment Planning Domain Planning Formalism Undesirable Behaviour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work has been funded in part through the Open FET MUSE project (FP7-296703). The contents of this paper only reflect the authors opinions and not necessarily the official position of Haute Autorité de Santé.

References

  1. 1.
    Bickmore, T., Ring, L.: Making it personal: end-user authoring of health narratives delivered by virtual agents. In: Allbeck, J., Badler, N., Bickmore, T., Pelachaud, C., Safonova, A. (eds.) IVA 2010. LNCS, vol. 6356, pp. 399–405. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  2. 2.
    Bickmore, T., Schulman, D., Yin, L.: Engagement vs. deceit: virtual humans with human autobiographies. In: Ruttkay, Z., Kipp, M., Nijholt, A., Vilhjálmsson, H.H. (eds.) IVA 2009. LNCS, vol. 5773, pp. 6–19. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  3. 3.
    Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of the Biennial GSCL Conference, pp. 31–40 (2009)Google Scholar
  4. 4.
    Bradbrook, K., Winstanley, G., Glasspool, D.W., Fox, J., Griffiths, R.N.: AI planning technology as a component of computerised clinical practice guidelines. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds.) AIME 2005. LNCS (LNAI), vol. 3581, pp. 171–180. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  5. 5.
    Charles, F., Cavazza, M., Smith, C., Georg, G., Porteous, J.: Instantiating interactive narratives from patient education documents. In: Peek, N., Marín Morales, R., Peleg, M. (eds.) AIME 2013. LNCS, vol. 7885, pp. 273–283. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  6. 6.
    Cordar, A., Borish, M., Foster, A., Lok, B.: Building virtual humans with back stories: training interpersonal communication skills in medical students. In: Bickmore, T., Marsella, S., Sidner, C. (eds.) IVA 2014. LNCS, vol. 8637, pp. 144–153. Springer, Heidelberg (2014) Google Scholar
  7. 7.
    Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. MIT Press, Cambridge (1998) MATHGoogle Scholar
  8. 8.
    Georg, G., Cavazza, M.: Integrating document-based and knowledge-based models for clinical guidelines analysis. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds.) AIME 2007. LNCS (LNAI), vol. 4594, pp. 421–430. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  9. 9.
    Gerevini, A., Long, D.: Plan constraints and preferences in pddl3. The Language of the Fifth International Planning Competition. Technical report, Department of Electronics for Automation, University of Brescia, Italy, 75 (2005)Google Scholar
  10. 10.
    González-Ferrer, A., Ten Teije, A., Fdez-Olivares, J., Milian, K.: Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks. Artif. Intell. Med. 57(2), 91–109 (2013)CrossRefGoogle Scholar
  11. 11.
    Klatt, J., Marsella, S., Krämer, N.C.: Negotiations in the context of AIDS prevention: an agent-based model using theory of mind. In: Vilhjálmsson, H.H., Kopp, S., Marsella, S., Thórisson, K.R. (eds.) IVA 2011. LNCS, vol. 6895, pp. 209–215. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  12. 12.
    Magerko, B., Wray, R.E., Holt, L.S., Stensrud, B.: Customizing interactive training through individualized content and increased engagement. In: The Interservice/Industry Training, Simulation & Education Conference (I/ITSEC), number 1 (2005)Google Scholar
  13. 13.
    Miksch, S., Shahar, Y., Johnson, P.: Asbru: a task-specific, intention-based, and time-oriented language for representing skeletal plans. In: Proceedings of the 7th Workshop on Knowledge Engineering: Methods & Languages (KEML-97), Milton Keynes, UK, The Open University, pp. 9–19 (1997)Google Scholar
  14. 14.
    Miller, L., Appleby, P., Christensen, J., Godoy, C., Si, M., Corsbie-Massay, C., Noar, S., Harrington, N.: Virtual interactive interventions for reducing risky sex: adaptations, integrations, and innovations. In: eHealth Applications: Promising Strategies for Health Behavior Change, pp. 79–95. Routledge, New York (2012)Google Scholar
  15. 15.
    Pauls, A., Klein, D.: Faster and smaller n-gram language models. In: Proceedings of the 49th Meeting of the Association for Computational Linguistics, HLT 2011, pp. 258–267, Stroudsburg, PA, USA (2011)Google Scholar
  16. 16.
    Porteous, J., Cavazza, M., Charles, F.: Applying planning to interactive storytelling: narrative control using state constraints. ACM Trans. Intell. Syst. Technol. (TIST) 1(2), 10 (2010)Google Scholar
  17. 17.
    Porteous, J., Lindsay, A., Read, J., Truran, M., Cavazza, M.: Automated extension of narrative planning domains with antonymic operators. In: Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems, pp. 1547–1555. IFAAMAS (2015)Google Scholar
  18. 18.
    Safeer, R.S., Keenan, J.: Health literacy: the gap between physicians and patients. Am. Fam. Physician 72(3), 463–468 (2005)Google Scholar
  19. 19.
    Shahar, Y., Musen, M.A.: Plan recognition and revision in support of guideline-based care. In: Working notes of the AAAI Spring Symposium on Representing Mental States and Mechanisms, pp. 118–126 (1995)Google Scholar
  20. 20.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, NAACL 2003, Stroudsburg, PA, USA, vol. 1, pp. 173–180 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alan Lindsay
    • 1
  • Fred Charles
    • 1
  • Jonathon Read
    • 1
  • Julie Porteous
    • 1
  • Marc Cavazza
    • 1
  • Gersende Georg
    • 2
  1. 1.School of ComputingTeesside UniversityMiddlesbroughUK
  2. 2.Haute Autorité de SantéSaint-denisFrance

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