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Ontology-Driven, Adaptive, Medical Questionnaires for Patients with Mild Learning Disabilities

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Artificial Intelligence XXXVI (SGAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11927))

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.

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Notes

  1. 1.

    WHO: Definition: intellectual disability, https://bit.ly/2qFOwFX (2010).

  2. 2.

    https://www.who.int/classifications/icf/en/.

  3. 3.

    https://www.w3.org/WAI/standards-guidelines/wcag/.

  4. 4.

    Turning Point: Learning Disability Health Toolkit, https://bit.ly/2JgecS0 (2016).

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Authors and Affiliations

  1. University of Strathclyde, Glasgow, UK

    Ryan Colin Gibson & Mark D. Dunlop

  2. University of Edinburgh, Edinburgh, UK

    Matt-Mouley Bouamrane

Authors
  1. Ryan Colin Gibson
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  2. Matt-Mouley Bouamrane
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  3. Mark D. Dunlop
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Corresponding author

Correspondence to Ryan Colin Gibson .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Gibson, R.C., Bouamrane, MM., Dunlop, M.D. (2019). Ontology-Driven, Adaptive, Medical Questionnaires for Patients with Mild Learning Disabilities. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_8

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  • DOI: https://doi.org/10.1007/978-3-030-34885-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34884-7

  • Online ISBN: 978-3-030-34885-4

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