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Zhang Z, Yu P, Yin M, Chang HC, Thomas SJ, Wei W, Song T, Deng C. Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia. Sci Rep 2024; 14:1937. [PMID: 38253678 PMCID: PMC10803746 DOI: 10.1038/s41598-023-46226-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 10/30/2023] [Indexed: 01/24/2024] Open
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.
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Affiliation(s)
- Zhenyu Zhang
- Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia
| | - Ping Yu
- Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia.
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia.
| | - Mengyang Yin
- Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia
- Systems and Reporting Residential Care, Catholic Healthcare Ltd, Wollongong, Australia
| | - Hui Chen Chang
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia
- School of Nursing, University of Wollongong, Wollongong, Australia
| | - Susan J Thomas
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia
- Graduate School of Medicine, University of Wollongong, Wollongong, Australia
| | - Wenxi Wei
- School of Nursing, University of Wollongong, Wollongong, Australia
| | - Ting Song
- Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia
| | - Chao Deng
- Illawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia
- School of Medical, Indigenous and Health Sciences, University of Wollongong, Wollongong, Australia
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Zhu Y, Song T, Zhang Z, Deng C, Alkhalaf M, Li W, Yin M, Chang HCR, Yu P. Agitation Prevalence in People With Dementia in Australian Residential Aged Care Facilities: Findings From Machine Learning of Electronic Health Records. J Gerontol Nurs 2022; 48:57-64. [PMID: 35343838 DOI: 10.3928/00989134-20220309-01] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
Using a suite of artificial intelligence technologies, the current study sought to determine the prevalence of agitated behaviors in people with dementia in residential aged care facilities (RACFs) in Australia. Computerized natural language processing allowed extraction of agitation instances from the free-text nursing progress notes, a component of electronic health records in RACFs. In total, 59 observable agitated behaviors were found. No difference was found in dementia prevalence between female and male clients (44.1%), across metropolitan and regional facilities (42.1% [SD = 17.9%]), or for agitation prevalence in dementia (76.5% [SD = 18.4%]). The top 10 behaviors were resisting, wandering, speaking in excessively loud voice, pacing, restlessness, pushing, shouting, complaining, frustration, and using profane language. Four to 17 agitated behaviors coexisted in 53% of people with dementia agitation, indicating high caregiver burden in these RACFs. Improving workforce training and redesigning care models are urgent for sustainability of dementia care in RACFs. [Journal of Gerontological Nursing, 48(4), 57-64.].
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Zhang Z, Yu P, Pai N, Chang HCR, Chen S, Yin M, Song T, Lau SK, Deng C. Developing an Intuitive Graph Representation of Knowledge for Nonpharmacological Treatment of Psychotic Symptoms in Dementia. J Gerontol Nurs 2022; 48:49-55. [PMID: 35343842 DOI: 10.3928/00989134-20220308-02] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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. [Journal of Gerontological Nursing, 48(4), 49-55.].
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Devi R, Mehrotra D, Lamine SBAB. Constituent vs Dependency Parsing-Based RDF Model Generation from Dengue Patients’ Case Sheets. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2022. [DOI: 10.1142/s0219649222500137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Electronic Health Record (EHR) systems in healthcare organisations are primarily maintained in isolation from each other that makes interoperability of unstructured(text) data stored in these EHR systems challenging in the healthcare domain. Similar information may be described using different terminologies by different applications that can be evaded by transforming the content into the Resource Description Framework (RDF) model that is interoperable amongst organisations. RDF requires a document’s contents to be translated into a repository of triplets (subject, predicate, object) known as RDF statements. Natural Language Processing (NLP) techniques can help get actionable insights from these text data and create triplets for RDF model generation. This paper discusses two NLP-based approaches to generate the RDF models from unstructured patients’ documents, namely dependency structure-based and constituent(phrase) structure-based parser. Models generated by both approaches are evaluated in two aspects: exhaustiveness of the represented knowledge and the model generation time. The precision measure is used to compute the models’ exhaustiveness in terms of the number of facts that are transformed into RDF representations.
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Affiliation(s)
- Runumi Devi
- School of Computing Science and Engineering, Galgotias University, Yamuna Expressway, Greater Noida, Gautam Buddh Nagar, Uttar Pradesh, India
- Amity Institute of Information Technology, Amity University Uttar Pradesh, Noida, India
| | - Deepti Mehrotra
- Amity School of Engineering and Technology, Amity University Uttar Pradesh, Noida, India
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Zhang Z, Yu P, Chang HCR, Lau SK, Tao C, Wang N, Yin M, Deng C. Developing an ontology for representing the domain knowledge specific to non-pharmacological treatment for agitation in dementia. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2020; 6:e12061. [PMID: 32995470 PMCID: PMC7507392 DOI: 10.1002/trc2.12061] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 06/19/2020] [Accepted: 07/09/2020] [Indexed: 11/12/2022]
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 the standards for biomedical ontology. DISCUSSION DRANPTO is the first comprehensive semantic representation of non-pharmacological 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.
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Affiliation(s)
- Zhenyu Zhang
- Centre for Digital Transformation School of Computing and Information Technology University of Wollongong Wollongong New South Wales Australia
| | - Ping Yu
- Centre for Digital Transformation School of Computing and Information Technology University of Wollongong Wollongong New South Wales Australia
- Illawarra Health and Medical Research Institute Wollongong New South Wales Australia
| | - Hui Chen Rita Chang
- Illawarra Health and Medical Research Institute Wollongong New South Wales Australia
- School of Nursing University of Wollongong Wollongong New South Wales Australia
| | - Sim Kim Lau
- Centre for Digital Transformation School of Computing and Information Technology University of Wollongong Wollongong New South Wales Australia
| | - Cui Tao
- School of Biomedical Informatics University of Texas Health Science Center Houston Texas USA
| | - Ning Wang
- PR China Southern Centre for Evidence Based Nursing and Midwifery Practice School of Nursing Southern Medical University Guangzhou City PR China
| | - Mengyang Yin
- Systems and Reporting Residential Care Catholic Healthcare Ltd Macquarie Park New South Wales Australia
| | - Chao Deng
- Illawarra Health and Medical Research Institute Wollongong New South Wales Australia
- School of Medicine University of Wollongong Wollongong New South Wales Australia
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