Visualization of Patient Behavior from Natural Language Recommendations.

Siddle, J., Lindsay, A., Ferreira, J., Porteous, J., Read, J., Charles, F., Cavazza, M. and Georg, G., 2017. Visualization of Patient Behavior from Natural Language Recommendations. In: K-Cap 2017: Proceedings of International Conference on Knowledge Capture, 4 - 6 December 2017, Austin, Texas, USA. (In Press)

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DOI: 10.1145/3148011.3148036

Abstract

The visualization of procedural knowledge from textual documents using 3D animation may be a way to improve understanding. We are interested in applying this approach to documents relating to patient education for bariatric surgery: a domain with challenging textual documents describing behavior recommendations that contain few procedural steps and leave much commonsense knowledge unspecified. In this work we look at how to automatically capture knowledge from a range of differently phrased recommendations and use that with implicit knowledge about compliance and violation, such that the recommendations can be visualized using 3D animations. Our solution is an end-to-end system that automates this process via: analysis of input recommendations to uncover their conditional structure; the use of commonsense knowledge and deontic logic to generate compliance and violation rules; and mapping of this knowledge to update a default knowledge base, which is used to generate appropriate sequences of visualizations. In this paper we overview this approach and demonstrate its potential.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:NLP; Default Reasoning; Deontic Modality; AI Planning; Serious Games; Interactive Narrative; Virtual Reality
Group:Faculty of Science & Technology
ID Code:29832
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:11 Oct 2017 13:38
Last Modified:11 Oct 2017 13:38

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