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In Alzheimer's & dementia (New York, N. Y.)

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.

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


agitation, artificial intelligence, dementia, knowledge base, knowledge representation, long‐term care, non‐pharmacological treatment, ontology, semantic web