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
Methods: The resultant ontology—Dementia-Related Agitation Non-Pharmacological
Treatment Ontology (DRANPTO)—was developed using a method adopted from the
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.