Evidence map›Paper›PMID 38028962›Full record

ArticleHealth information science and systems2023

ADHD-KG: a knowledge graph of attention deficit hyperactivity disorder.

Emmanuel Papadakis, George Baryannis, Sotiris Batsakis, Marios Adamou, Zhisheng Huang, Grigoris Antoniou

Open access · hybridAbstract read
In one paragraph

Article in Health information science and systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. 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

6 authors at 3 institutions in 2 countries.

Emmanuel PapadakisSchool of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH West Yorkshire UK.ORCID 0000-0001-8669-2420
George BaryannisSchool of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH West Yorkshire UK.
Sotiris BatsakisSchool of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH West Yorkshire UK.
Marios AdamouSouth West Yorkshire Partnership NHS Foundation Trust, Portobello Road, Wakefield, WF1 5PN West Yorkshire UK.
Zhisheng HuangDepartment of Computer Science, Vrije University of Amsterdam, De Boelelaan 1081, Amsterdam, 1081 HV Netherlands.
Grigoris AntoniouSchool of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield, HD1 3DH West Yorkshire UK.
University of Huddersfield · GBSouth West Yorkshire Partnership NHS Foundation Trust · GBVrije Universiteit Amsterdam · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Attention Deficit Hyperactivity Disorder (ADHD) is a widespread condition that affects human behaviour and can interfere with daily activities and relationships. Medication or medical information about ADHD can be found in several data sources on the Web. Such distribution of knowledge raises notable obstacles since researchers and clinicians must manually combine various sources to deeply explore aspects of ADHD. Knowledge graphs have been widely used in medical applications due to their data integration capabilities, offering rich data stores of information built from heterogeneous sources; however, general purpose knowledge graphs cannot represent knowledge in sufficient detail, thus there is an increasing interest in domain-specific knowledge graphs. Methods: In this work we propose a Knowledge Graph of ADHD. In particular, we introduce an automated procedure enabling the construction of a knowledge graph that covers knowledge from a wide range of data sources primarily focusing on adult ADHD. These include relevant literature and clinical trials, prescribed medication and their known side-effects. Data integration between these data sources is accomplished by employing a suite of information linking procedures, which aim to connect resources by relating them to common concepts found in medical thesauri. Results: The usability and appropriateness of the developed knowledge graph is evaluated through a series of use cases that illustrate its ability to enhance and accelerate information retrieval. Conclusion: The Knowledge Graph of ADHD can provide valuable assistance to researchers and clinicians in the research, training, diagnostic and treatment processes for ADHD.

Indexed as

Adult ADHDAttention deficit hyperactivity disorderInformation linkingKnowledge graphMental health

Identifiers

PMID38028962
PMCPMC10640525
OpenAlexW4388601256

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.