Ontology-driven, adaptive, medical questionnaires for patients with mild learning disabilities

Gibson, Ryan Colin and Bouamrane, Matt-Mouley and Dunlop, Mark D. (2019) Ontology-driven, adaptive, medical questionnaires for patients with mild learning disabilities. In: Artifical Intelligence XXXVI, 2019-12-17 - 2019-12-19, University of Cambridge.

[img]
Preview
 Text (Gibson-etal-ICAI-2019-Ontology-driven-adaptive-medical-questionnaires-for-patients-with-mild-learning-disabilities)
Gibson_etal_ICAI_2019_Ontology_driven_adaptive_medical_questionnaires_for_patients_with_mild_learning_disabilities.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo
Download (798kB)| Preview

    Abstract

    Patients with Learning Disabilities (LD) have substantial and unmet healthcare needs, and previous studies have highlighted that they face both health inequalities and worse outcomes than the general population. Primary care practitioners are often the first port-of-call for medical consultations, and one issue faced by LD patients in this context is the very limited time available during consultations - typically less than ten minutes. In order to alleviate this issue, we propose a digital communication aid in the form of an ontology-based medical questionnaire that can adapt to a patient’s medical context as well as their accessibility needs (physical and cognitive). The application is intended to be used in advance of a consultation so that a primary care practitioner may have prior access to their LD patients’ self-reported symptoms. This work builds upon and extends previous research carried out in the development of adaptive medical questionnaires to include interactive and interface functionalities designed specifically to cater for patients with potentially complex accessibility needs. A patient’s current health status and accessibility profile (relating to their impairments) is used to dynamically adjust the structure and content of the medical questionnaire. As such, the system is able to significantly limit and focus questions to immediately relevant concerns while discarding irrelevant questions. We propose that our ontology-based design not only improves the relevance and accessibility of medical questionnaires for patients with LDs, but also provides important benefits in terms of medical knowledge-base modularity, as well as for software extension and maintenance.

    Creators(s): Gibson, Ryan Colin ORCID logoORCID: https://orcid.org/0000-0002-0425-1486, Bouamrane, Matt-Mouley ORCID logoORCID: https://orcid.org/0000-0002-1416-751X and Dunlop, Mark D. ORCID logoORCID: https://orcid.org/0000-0002-3668-485X;
    Item type:Conference or Workshop Item(Paper)
    ID code:70972
    Keywords:OWL ontologies, adaptive questionnaires, intelligent software development, knowledge-base modularity, HCI, human-computer interaction, accessibility, digital communication aids, learning disabilities, Electronic computers. Computer science, Human-Computer Interaction
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Department:Faculty of Science > Computer and Information Sciences
    Depositing user: Pure Administrator
    Date deposited:18 Dec 2019 11:40
    Last modified:02 Oct 2020 03:17
    URI:https://strathprints.strath.ac.uk/id/eprint/70972
    Export data:
    CORE (COnnecting REpositories)