Visualization of patient behavior from natural language recommendations

Jonathan Siddle, Alan Lindsay, João F. Ferreira, Julie Porteous, Jonathon Read, Fred Charles, Marc Cavazza, Gersende Georg

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationProceedings of K-CAP 2017
Subtitle of host publicationKnowledge Capture Conference (K-CAP 2017)
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Electronic)9781450355537
Publication statusPublished - 4 Dec 2017
Externally publishedYes
Event9th International Conference on Knowledge Capture - Hilton Garden Inn Convention Center, Austin, United States
Duration: 4 Dec 20176 Dec 2017
Conference number: 9 (Link to Conference Website)


Conference9th International Conference on Knowledge Capture
Abbreviated titleK-CAP 2017
Country/TerritoryUnited States
Internet address


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