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

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 t

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC7507392 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature