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dc.contributor.authorJammeh, Emmanuel
dc.contributor.authorZhao, P
dc.contributor.authorCarroll, Camille
dc.contributor.authorPearson, S
dc.contributor.authorIfeachor, Emmanuel

Early diagnosis of Alzheimer's Disease (AD) is widely regarded as necessary to allow treatment to be started before irreversible damage to the brain occur and for patients to benefit from new therapies as they become available. Low-cost point-of-care (PoC) diagnostic tools that can be used to routinely diagnose AD in its early stage would facilitate this, but such tools require reliable and accurate biomarkers. However, traditional biomarkers for AD use invasive cerebrospinal fluid (CSF) analysis and/or expensive neuroimaging techniques together with neuropsychological assessments. Blood-based PoC diagnostics tools may provide a more cost and time efficient way to assess AD to complement CSF and neuroimaging techniques. However, evidence to date suggests that only a panel of biomarkers would provide the diagnostic accuracy needed in clinical practice and that the number of biomarkers in such panels can be large. In addition, the biomarkers in a panel vary from study to study. These issues make it difficult to realise a PoC device for diagnosis of AD. An objective of this paper is to find an optimum number of blood biomarkers (in terms of number of biomarkers and sensitivity/specificity) that can be used in a handheld PoC device for AD diagnosis. We used the Alzheimer's disease Neuroimaging Initiative (ADNI) database to identify a small number of blood biomarkers for AD. We identified a 6-biomarker panel (which includes A1Micro, A2Macro, AAT, ApoE, complement C3 and PPP), which when used with age as covariate, was able to discriminate between AD patients and normal subjects with a sensitivity of 85.4% and specificity of 78.6%.

dc.subjectMachine Learning
dc.titleIdentification of blood biomarkers for use in point of care diagnosis tool for Alzheimer's disease
dc.typeJournal Article
plymouth.journal2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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.placeUnited States
dc.rights.embargoperiodNo embargo
rioxxterms.identifier.projectNovel Point-of-Care Diagnostic Techniques for Dementia
rioxxterms.typeJournal Article/Review
plymouth.funderNovel Point-of-Care Diagnostic Techniques for Dementia::EPSRC

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