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

Zhenyu Zhang; Ping Yu; Hui Chen (Rita) Chang; Sim Kim Lau; Cui Tao; Ning Wang; Mengyang Yin; Chao Deng

doi:10.1002/trc2.12061

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

Zhenyu Zhang
, Ping Yu
, Hui Chen (Rita) Chang
, Sim Kim Lau
, Cui Tao
, Ning Wang
, Mengyang Yin
, Chao Deng

Research output: Contribution to journal › Article › peer-review

16 Citations (Scopus)

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 non-pharmacological 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.

Original language	English
Article number	e12061
Number of pages	17
Journal	Alzheimer's and Dementia: Translational Research and Clinical Interventions
Volume	6
Issue number	1
DOIs	https://doi.org/10.1002/trc2.12061
Publication status	Published - 2020

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© 2020 The Authors. Alzheimer's & Dementia: Translational Research & Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association

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This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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10.1002/trc2.12061

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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{"}{\textquotedblright}Dementia-Related Agitation Non-Pharmacological Treatment Ontology (DRANPTO){"}{\textquotedblright}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 non-pharmacological 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.",

author = "Zhenyu Zhang and Ping Yu and Chang, \{Hui Chen (Rita)\} and Lau, \{Sim Kim\} and Cui Tao and Ning Wang and Mengyang Yin and Chao Deng",

note = "Publisher Copyright: {\textcopyright} 2020 The Authors. Alzheimer's \& Dementia: Translational Research \& Clinical Interventions published by Wiley Periodicals LLC on behalf of Alzheimer's Association",

year = "2020",

doi = "10.1002/trc2.12061",

language = "English",

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journal = "Alzheimer's and Dementia: Translational Research and Clinical Interventions",

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T1 - Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia

AU - Zhang, Zhenyu

AU - Yu, Ping

AU - Chang, Hui Chen (Rita)

AU - Lau, Sim Kim

AU - Tao, Cui

AU - Wang, Ning

AU - Yin, Mengyang

AU - Deng, Chao

PY - 2020

Y1 - 2020

N2 - 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 non-pharmacological 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.

AB - 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 non-pharmacological 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.

UR - https://hdl.handle.net/1959.7/uws:69611

U2 - 10.1002/trc2.12061

DO - 10.1002/trc2.12061

M3 - Article

SN - 2352-8737

VL - 6

JO - Alzheimer's and Dementia: Translational Research and Clinical Interventions

JF - Alzheimer's and Dementia: Translational Research and Clinical Interventions

IS - 1

M1 - e12061

ER -

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

Abstract

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