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Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia

Journal Article


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Abstract


  • Introduction: A large volume of clinical care data has been generated for managing

    agitation in dementia. However, the valuable information in these data has not been

    used effectively to generate insights for improving the quality of care. Application of

    artificial intelligence technologies offers us enormous opportunities to reuse these

    data. For health data science to achieve this, this study focuses on using ontology

    to coding clinical knowledge for non-pharmacological treatment of agitation in a

    machine-readable format.

    Methods: The resultant ontology—Dementia-Related Agitation Non-Pharmacological

    Treatment Ontology (DRANPTO)—was developed using a method adopted from the

    NeOn methodology.

    Results: DRANPTO consisted of 569 concepts and 48 object properties. It meets the

    standards for biomedical ontology.

    Discussion: DRANPTO is the first comprehensive semantic representation of nonpharmacological

    management for agitation in dementia in the long-term care setting.

    As a knowledge base, it will play a vital role to facilitate the development of intelligent

    systems for managing agitation in dementia.

Authors


Publication Date


  • 2020

Citation


  • Zhang, Z., Yu, P., Chang, H. C., Lau, S. K., Tao, C., Wang, N., Yin, M. & Deng, C. (2020). Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia. Alzheimer's & Dementia, 6 (1), e12061-1-e12061-17.

Scopus Eid


  • 2-s2.0-85095948901

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5373&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4344

Start Page


  • e12061-1

End Page


  • e12061-17

Volume


  • 6

Issue


  • 1

Place Of Publication


  • United States

Abstract


  • Introduction: A large volume of clinical care data has been generated for managing

    agitation in dementia. However, the valuable information in these data has not been

    used effectively to generate insights for improving the quality of care. Application of

    artificial intelligence technologies offers us enormous opportunities to reuse these

    data. For health data science to achieve this, this study focuses on using ontology

    to coding clinical knowledge for non-pharmacological treatment of agitation in a

    machine-readable format.

    Methods: The resultant ontology—Dementia-Related Agitation Non-Pharmacological

    Treatment Ontology (DRANPTO)—was developed using a method adopted from the

    NeOn methodology.

    Results: DRANPTO consisted of 569 concepts and 48 object properties. It meets the

    standards for biomedical ontology.

    Discussion: DRANPTO is the first comprehensive semantic representation of nonpharmacological

    management for agitation in dementia in the long-term care setting.

    As a knowledge base, it will play a vital role to facilitate the development of intelligent

    systems for managing agitation in dementia.

Authors


Publication Date


  • 2020

Citation


  • Zhang, Z., Yu, P., Chang, H. C., Lau, S. K., Tao, C., Wang, N., Yin, M. & Deng, C. (2020). Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia. Alzheimer's & Dementia, 6 (1), e12061-1-e12061-17.

Scopus Eid


  • 2-s2.0-85095948901

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=5373&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/4344

Start Page


  • e12061-1

End Page


  • e12061-17

Volume


  • 6

Issue


  • 1

Place Of Publication


  • United States