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

Zhang, Zhenyu; Yu, Ping; Chang, Hui‐Chen; Lau, Sim Kim; Tao, Cui; Wang, Ning; Yin, Mengyang; Deng, Chao

doi:10.1002/trc2.12061

Cited by 13 publications

(10 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In line with former studies [13,30], the expert-based evaluation ensured the quality of the STMSO. Although this is a subjective evaluation approach, it is frequently considered a good validation process because it relies on the deep knowledge of external experts who can explore the quality of an ontology.…”

Section: Discussionmentioning

confidence: 59%

“…All treatment classes are related to each symptom through Semi-automatic approaches to knowledge extraction have been developed to build ontologies from texts. For example, Dostal et al [29] used statistical and natural language processing techniques and automatic approaches to create domain ontologies, and we systematically collected and selected the relevant literature similar to Zhang et al [30]. This approach ensures a credible and comprehensive knowledge base for the formulation of the ontology and makes the ontology complete and more coherent.…”

Section: Discussionmentioning

confidence: 99%

“…The treatment class was an important class in our study that also existed in previous treatment ontologies in other domains [13,30]. We also included non-pharmacological treatment, with subclasses such as exercise therapy and music therapy that are similar to those in the diabetes mellitus treatment ontology [13].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Ontology for Symptomatic Treatment of Multiple Sclerosis

2022

View full text Add to dashboard Cite

Objectives: Symptomatic treatment is an essential component in the overall treatment of multiple sclerosis (MS). However, knowledge in this regard is confusing and scattered. Physicians also have challenges in choosing symptomatic treatment based on the patient’s condition. To share, update, and reuse this knowledge, the aim of this study was to provide an ontology for MS symptomatic treatment.Methods: The Symptomatic Treatment of Multiple Sclerosis Ontology (STMSO) was developed according to Ontology Development 101 and a guideline for developing good ontologies in the biomedical domain. We obtained knowledge and rules through a systematic review and entered this knowledge in the form of classes and subclasses in the ontology. We then mapped the ontology using the Basic Formal Ontology (BFO) and Ontology for General Medical Sciences (OGMS) as reference ontologies. The ontology was built using Protégé Editor in the Web Ontology Language format. Finally, an evaluation was done by experts using criterion-based approaches in terms of accuracy, clarity, consistency, and completeness.Results: The knowledge extraction phase identified 110 articles related to the ontology in the form of 626 classes, 40 object properties, and 139 rules. Five general classes included “patient,” “symptoms,” “pharmacological treatment,” “treatment plan,” and “measurement index.” The evaluation in terms of standards for biomedical ontology showed that STMSO was accurate, clear, consistent, and complete.Conclusions: STMSO is the first comprehensive semantic representation of the symptomatic treatment of MS and provides a major step toward the development of intelligent clinical decision support systems for symptomatic MS treatment.

show abstract

Section: Discussionmentioning

confidence: 59%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Ontology for Symptomatic Treatment of Multiple Sclerosis

2022

View full text Add to dashboard Cite

show abstract

“…The study begins by examining the taxonomic process using the taxonomic levels in [30,31]. This is an ordering system that allows a set of elements to be grouped within certain predefined categories.…”

Section: Taxonomymentioning

confidence: 99%

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

et al. 2023

View full text Add to dashboard Cite

The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of an Alzheimer’s patient in the categories of wandering, nervous, depressed, disoriented or bored. The patients are from elderly care centers in Ambato Canton-Ecuador. The population consists of 147 individuals of both sexes, diagnosed with Alzheimer’s disease, with ages ranging from 75 to 89 years. The methods used are the taxonomic levels, the semantic categories and the ontological primitives. All these aspects allow the computational generation of an ontological structure, in addition to the use of the proprietary tool Pellet Reasoner as well as Apache NetBeans from Java for process completion. As a result, an ontological model is generated using its instances and Pellet Reasoner to identify the expected effect. It is noted that the ontologies come from the artificial intelligence domain. In this case, they are represented by aspects of real-world context that relate to common vocabularies for humans and applications working in a domain or area of interest.

show abstract

“…Moreover, ontology has been widely applied to many domains, such as health, finance, and geology 35 . In the past 40 years, there has been increasing use of ontologies to represent knowledge in the health domain, e.g., Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT) 36 , Gene Ontology 37 , International Classification of Diseases (ICD) ontology 38 , Dementia-Related Agitation Non-Pharmacological Treatment Ontology (DRANPTO) 39 , and Dementia-Related Psychotic Symptom Non-Pharmacological Treatment Ontology (DRPSNPTO) 40 . However, to the best of our knowledge, no ontology currently represents the knowledge in the domain of non-pharmacological treatment of emotional and mood disturbances in dementia.…”

Section: Introductionmentioning

confidence: 99%

Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia

Zhang,

Yu,

Yin

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 13 publications

References 68 publications

Ontology for Symptomatic Treatment of Multiple Sclerosis

Ontology for Symptomatic Treatment of Multiple Sclerosis

Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

Developing an ontology of non-pharmacological treatment for emotional and mood disturbances in dementia

Contact Info

Product

Resources

About