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dc.contributor.authorJammeh, Emmanuel
dc.contributor.authorCarroll, Camille
dc.contributor.authorPearson, SW
dc.contributor.authorEscudero, J
dc.contributor.authorAnastasiou, A
dc.contributor.authorZhao, P
dc.contributor.authorChenore, T
dc.contributor.authorZajicek, J
dc.contributor.authorIfeachor, Emmanuel
dc.date.accessioned2018-06-18T14:05:36Z
dc.date.available2018-06-18T14:05:36Z
dc.date.issued2018-07
dc.identifier.issn1849-5435
dc.identifier.issn2398-3795
dc.identifier.otherbjgpopen18X101589
dc.identifier.urihttp://hdl.handle.net/10026.1/11675
dc.description.abstract

<jats:sec><jats:title>Background</jats:title><jats:p>Up to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.</jats:p></jats:sec><jats:sec><jats:title>Aim</jats:title><jats:p>The aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.</jats:p></jats:sec><jats:sec><jats:title>Design &amp; setting</jats:title><jats:p>The study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>Routinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged &gt;65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.</jats:p></jats:sec>

dc.format.extent0-0
dc.format.mediumElectronic-eCollection
dc.languageen
dc.language.isoen
dc.publisherRoyal College of General Practitioners
dc.subjectGP practice
dc.subjectNHS data
dc.subjectRead code
dc.subjectdementia
dc.subjectmachine learning
dc.subjectprimary care
dc.titleMachine-learning based identification of undiagnosed dementia in primary care: a feasibility study
dc.typejournal-article
dc.typeJournal Article
plymouth.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/30564722
plymouth.issue2
plymouth.volume2
plymouth.publication-statusPublished
plymouth.journalBJGP Open
dc.identifier.doi10.3399/bjgpopen18X101589
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Health
plymouth.organisational-group/Plymouth/Faculty of Health/Peninsula Medical School
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA03 Allied Health Professions, Dentistry, Nursing and Pharmacy
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering
plymouth.organisational-group/Plymouth/Research Groups
plymouth.organisational-group/Plymouth/Research Groups/FoH - Applied Parkinson's Research
plymouth.organisational-group/Plymouth/Research Groups/FoH - Community and Primary Care
plymouth.organisational-group/Plymouth/Research Groups/Institute of Translational and Stratified Medicine (ITSMED)
plymouth.organisational-group/Plymouth/Research Groups/Institute of Translational and Stratified Medicine (ITSMED)/CCT&PS
plymouth.organisational-group/Plymouth/Research Groups/Plymouth Institute of Health and Care Research (PIHR)
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
plymouth.organisational-group/Plymouth/Users by role/Researchers in ResearchFish submission
dc.publisher.placeEngland
dcterms.dateAccepted2018-03-01
dc.identifier.eissn2398-3795
dc.rights.embargoperiodNo embargo
rioxxterms.funderEPSRC
rioxxterms.identifier.projectNovel Point-of-Care Diagnostic Techniques for Dementia
rioxxterms.versionofrecord10.3399/bjgpopen18X101589
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-07
rioxxterms.typeJournal Article/Review
plymouth.funderNovel Point-of-Care Diagnostic Techniques for Dementia::EPSRC


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