Developing an intuitive graph representation of knowledge for nonpharmacological treatment of psychotic symptoms in dementia

Zhenyu Zhang, Ping Yu, Nagesh Pai, Hui Chen (Rita) Chang, Shiyan Chen, Mengyang Yin, Ting Song, Sim Kim Lau, Chao Deng

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Applying person-centered, nonpharmacological interventions to manage psychotic symptoms of dementia is promoted for health care professionals, particularly gerontological nurses, who are responsible for care of older adults in nursing homes. A knowledge graph is a graph consisting of a set of concepts that are linked together by their interrelationship and has been widely used as a formal representation of domain knowledge in health. However, there is lack of a knowledge graph for nonpharmacological treatment of psychotic symptoms in dementia. Therefore, we developed a comprehensive, human- and machine-understandable knowledge graph for this domain, named Dementia-Related Psychotic Symptom Nonpharmacological Treatment Ontology (DRPSNPTO). This graph was built by adopting the established NeOn methodology, a knowledge graph engineering method, to meet the quality standards for biomedical knowledge graphs. This intuitive graph representation of the domain knowledge sets a new direction for visualizing and computerizing gerontological knowledge to facilitate human comprehension and build intelligent aged care information systems.
Original languageEnglish
Pages (from-to)49-55
Number of pages7
JournalJournal of Gerontological Nursing
Volume48
Issue number4
DOIs
Publication statusPublished - 2022

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