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

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.