Evidence map›Paper›PMID 38253678›Full record

ArticleScientific reports2024

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

Zhenyu Zhang, Ping Yu, Mengyang Yin, Hui Chen Chang, Susan J Thomas, Wenxi Wei, Ting Song, Chao Deng

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.5field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Zhenyu ZhangCentre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia.
Ping YuCentre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia. ping@uow.edu.au.
Mengyang YinCentre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia.
Hui Chen ChangIllawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia.
Susan J ThomasIllawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia.
Wenxi WeiSchool of Nursing, University of Wollongong, Wollongong, Australia.
Ting SongCentre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Northfield Ave, Wollongong, NSW, 2522, Australia.
Chao DengIllawarra Health and Medical Research Institute, University of Wollongong, Wollongong, Australia.
University of Wollongong · AUIllawarra Health and Medical Research Institute · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Biological OntologiesDementiaEmotionsHealth PersonnelHumansMood Disorders

Identifiers

PMID38253678
PMCPMC10803746
OpenAlexW4391091856

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.