Open AccessDeveloping an ontology for representing the domain knowledge specific to non‐pharmacological treatment for agitation in dementia
Author(s) -
Zhang Zhenyu,
Yu Ping,
Chang Hui Chen Rita,
Lau Sim Kim,
Tao Cui,
Wang Ning,
Yin Mengyang,
Deng Chao
Publication year - 2020
Publication title -
alzheimer's and dementia: translational research and clinical interventions
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.49
H-Index - 30
ISSN - 2352-8737
DOI - 10.1002/trc2.12061
Subject(s) - ontology , dementia , computer science , reuse , knowledge management , data science , medicine , disease , engineering , philosophy , epistemology , pathology , waste management
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.