Identifying Key Predictors of Cognitive Dysfunction in Older People Using Supervised Machine Learning Techniques: Observational Study
- Submitting institution
-
University of Ulster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 85781947
- Type
- D - Journal article
- DOI
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10.2196/20995
- Title of journal
- JMIR Medical Informatics
- Article number
- e20995
- First page
- 1
- Volume
- 8
- Issue
- 9
- ISSN
- 2291-9694
- Open access status
- Compliant
- Month of publication
- September
- 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
- No
- Number of additional authors
-
11
- Research group(s)
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A - Intelligent Systems Research Centre
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- <24> eHealth & Data Analytics Dementia Pathfinder Programme with the HSCB eHealth Directorate results have been presented to a range of key stakeholders: clinicians and PPI at several Dementia Analytics Research User Group meetings (verification Programme Manager, soo.hun@hscni.net) and within the 2020 FASEB Conference on Folic Acid, Vitamin B12 and One-Carbon Metabolism. It is part of a cross-disciplinary body of work analysing the Trinity, Ulster, and Department of Agriculture (TUDA) commissioned dataset with collaborators at Trinity College, Dublin (Prof. Anne Molloy) and at St James Hospital, Dublin (Prof. Conal Cunningham). Author Flanagan was appointed to lecturer position at Ulster (2020).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -