Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia
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. He...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-46226-5 |
| _version_ | 1797276524886360064 |
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| author | Zhenyu Zhang Ping Yu Mengyang Yin Hui Chen Chang Susan J. Thomas Wenxi Wei Ting Song Chao Deng |
| author_facet | Zhenyu Zhang Ping Yu Mengyang Yin Hui Chen Chang Susan J. Thomas Wenxi Wei Ting Song Chao Deng |
| author_sort | Zhenyu Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T15:29:25Z |
| format | Article |
| id | doaj.art-1fe652ab3c8e4f4f9030c4a58a9efb1e |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:29:25Z |
| publishDate | 2024-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-1fe652ab3c8e4f4f9030c4a58a9efb1e2024-03-05T16:29:06ZengNature PortfolioScientific Reports2045-23222024-01-0114111210.1038/s41598-023-46226-5Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementiaZhenyu Zhang0Ping Yu1Mengyang Yin2Hui Chen Chang3Susan J. Thomas4Wenxi Wei5Ting Song6Chao Deng7Centre for Digital Transformation, School of Computing and Information Technology, University of WollongongCentre for Digital Transformation, School of Computing and Information Technology, University of WollongongCentre for Digital Transformation, School of Computing and Information Technology, University of WollongongIllawarra Health and Medical Research Institute, University of WollongongIllawarra Health and Medical Research Institute, University of WollongongSchool of Nursing, University of WollongongCentre for Digital Transformation, School of Computing and Information Technology, University of WollongongIllawarra Health and Medical Research Institute, University of WollongongAbstract 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.https://doi.org/10.1038/s41598-023-46226-5 |
| spellingShingle | Zhenyu Zhang Ping Yu Mengyang Yin Hui Chen Chang Susan J. Thomas Wenxi Wei Ting Song Chao Deng Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia Scientific Reports |
| title | Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia |
| title_full | Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia |
| title_fullStr | Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia |
| title_full_unstemmed | Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia |
| title_short | Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia |
| title_sort | developing an ontology of non pharmacological treatment for emotional and mood disturbances in dementia |
| url | https://doi.org/10.1038/s41598-023-46226-5 |
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