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.
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Notes
- 1.
The term Patient Guidelines is drawn from working documents of the Guidelines International Network (GIN), http://www.g-i-n.net/document-store/working-groups-documents/g-i-n-public/toolkit/toolkit-chapter-4.pdf.
- 2.
Merriam-Webster http://www.dictionaryapi.com; Big Huge Thesaurus http://words.bighugelabs.com; Power Thesaurus http://www.powerthesaurus.org.
- 3.
Wikipedia provides a broad coverage sample of language which is appropriate here because activities of daily living do not involve specialised terminology.
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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.
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Lindsay, A., Charles, F., Read, J., Porteous, J., Cavazza, M., Georg, G. (2015). Generation of Non-compliant Behaviour in Virtual Medical Narratives. In: Brinkman, WP., Broekens, J., Heylen, D. (eds) Intelligent Virtual Agents. IVA 2015. Lecture Notes in Computer Science(), vol 9238. Springer, Cham. https://doi.org/10.1007/978-3-319-21996-7_22
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DOI: https://doi.org/10.1007/978-3-319-21996-7_22
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