Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study
- Submitting institution
-
University of Plymouth
- Unit of assessment
- 12 - Engineering
- Output identifier
- 107
- Type
- D - Journal article
- DOI
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10.3399/bjgpopen18X101589
- Title of journal
- BJGP Open
- Article number
- bjgpopen18X101589
- First page
- -
- Volume
- 2
- Issue
- 2
- ISSN
- 2398-3795
- Open access status
- Compliant
- Month of publication
- -
- Year of publication
- 2018
- 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
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8
- Research group(s)
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-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Improved dementia diagnosis is a global priority with up to 50% of patients living with the disease but not having access to care pathways. This paper provides, for the first time, an accurate method to automate the detection of undiagnosed dementia from routine healthcare data. It details a cost-effective way to identify those who may benefit from new therapies and was the most highly read article in the journal in the year of its publication (https://twitter.com/BLGPOpen/status/108876913719552).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -