| Autor: |
Zhenyu Zhang, Ping Yu, Mengyang Yin, Hui Chen Chang, Susan J. Thomas, Wenxi Wei, Ting Song, Chao Deng |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-023-46226-5 |
| Popis: |
Abstract Emotional and mood disturbances are common in people with dementia. Non-pharmacological interventions are beneficial for managing these disturbances. However, effectively applying these interventions, particularly in the person-centred approach, is a complex and knowledge-intensive task. Healthcare professionals need the assistance of tools to obtain all relevant information that is often buried in a vast amount of clinical data to form a holistic understanding of the person for successfully applying non-pharmacological interventions. A machine-readable knowledge model, e.g., ontology, can codify the research evidence to underpin these tools. For the first time, this study aims to develop an ontology entitled Dementia-Related Emotional And Mood Disturbance Non-Pharmacological Treatment Ontology (DREAMDNPTO). DREAMDNPTO consists of 1258 unique classes (concepts) and 70 object properties that represent relationships between these classes. It meets the requirements and quality standards for biomedical ontology. As DREAMDNPTO provides a computerisable semantic representation of knowledge specific to non-pharmacological treatment for emotional and mood disturbances in dementia, it will facilitate the application of machine learning to this particular and important health domain of emotional and mood disturbance management for people with dementia. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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