Machine learning-based method for personalized and cost-effective detection of alzheimer's disease

IEEE Transactions on Biomedical Engineering

Volumn 60, Issue 1, 2013, Pages 164-168

  Escudero, Javier ^a   Ifeachor, Emmanuel ^a   Zajicek, John P ^b   Green, Colin ^c   Shearer, James ^c   Pearson, Stephen ^d  

^a UNIVERSITY OF PLYMOUTH   (United Kingdom)

^b PLYMOUTH UNIVERSITY PENINSULA SCHOOLS OF MEDICINE AND DENTISTRY   (United Kingdom)

^c UNIVERSITY OF EXETER   (United Kingdom)

^d DEVON PARTNERSHIP NHS TRUST   (United Kingdom)

Author keywords

Alzheimer's disease (AD); classification; cost; machine learning; mild cognitive impairment (MCI); personalization

Indexed keywords

ALZHEIMER'S DISEASE; CLASSIFIER MODELS; CLINICAL SETTINGS; COGNITIVE IMPAIRMENT; DISEASE PROCESS; LEARNING APPROACH; LEARNING-BASED METHODS; LOCALLY WEIGHTED LEARNING; PERSONALIZATIONS;

CLASSIFICATION (OF INFORMATION); COST EFFECTIVENESS; COSTS; LEARNING SYSTEMS;

DIAGNOSIS;

AMYLOID BETA PROTEIN[1-40]; AMYLOID BETA PROTEIN[1-42]; APOLIPOPROTEIN E4; HOMOCYSTEINE; TAU PROTEIN;

ALZHEIMER DISEASE; ARTICLE; CEREBROSPINAL FLUID ANALYSIS; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED STUDY; COST EFFECTIVENESS ANALYSIS; FOLLOW UP; FUNCTIONAL NEUROIMAGING; HUMAN; MACHINE LEARNING; MAJOR CLINICAL STUDY; MILD COGNITIVE IMPAIRMENT; NUCLEAR MAGNETIC RESONANCE IMAGING; PERSONALIZED MEDICINE; POSITRON EMISSION TOMOGRAPHY; PROTEIN BLOOD LEVEL; PROTEIN PHOSPHORYLATION; SENSITIVITY AND SPECIFICITY;

ALZHEIMER DISEASE; ARTIFICIAL INTELLIGENCE; BIOLOGICAL MARKERS; COST-BENEFIT ANALYSIS; DATABASES, FACTUAL; DISEASE PROGRESSION; FEMALE; HUMANS; INDIVIDUALIZED MEDICINE; MAGNETIC RESONANCE IMAGING; MALE; MILD COGNITIVE IMPAIRMENT; REPRODUCIBILITY OF RESULTS;

EID: 84871813342     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2012.2212278     Document Type: Article

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