A hybrid AI approach for supporting clinical diagnosis of attention deficit hyperactivity disorder (ADHD) in adults
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 3
- Type
- D - Journal article
- DOI
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10.1007/s13755-020-00123-7
- Title of journal
- Health Information Science and Systems
- Article number
- 1
- First page
- -
- Volume
- 9
- Issue
- 1
- ISSN
- 2047-2501
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
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3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Published in a Scimago Q1 journal, the output reports on novel AI approaches to improve the diagnostic processes for ADHD in adults. The research uderpinned a collaborative project with the South-West Yorkshire NHS Partnership Foundation Trust (responsible person: Dr Adamou; marios.adamou@swyt.nhs.uk), and led Antoniou to obtain funded projects through Grow Medtech (https://growmed.tech/) and the NHS AI Lab under its inaugural AI for Health and Care Award (https://www.nihr.ac.uk/documents/ai-in-health-and-care-awards-funded-projects-2020/25625#Phase_1_projects).The research in this paper work was a direct contributor for a keynote talk by Antoniou at PRICAI 2019 (https://www.pricai.org/2019/program/2-uncategorised/35-keynotes-2019).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -