Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers

Models characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD.

• Publication year

  2018

• Venue

  Alzheimer's & Dementia

• Publication date

  2018-08-10

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1016/j.dadm.2018.06.007PMID 30456290PMCID 6234900
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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