publication . Article . Other literature type . 2014

Predicting Progression of Alzheimer's Disease Using Ordinal Regression

anita opoku; Magda Tsolaki; Simon Lovestone; Orla Doyle; Eric Westman; Steven Charles Rees Williams; Patrizia Mecocci; Andre Marquand;
Open Access
  • Published: 20 Aug 2014 Journal: PLoS ONE, volume 9, page e105542 (eissn: 1932-6203, Copyright policy)
  • Publisher: Public Library of Science (PLoS)
  • Country: Netherlands
We propose a novel approach to predicting disease progression in Alzheimer's disease (AD) - multivariate ordinal regression - which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression - the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed da...
Medical Subject Headings: mental disorders
free text keywords: Research Article, Biology and Life Sciences, Neuroscience, Cognitive Science, Artificial Intelligence, Computer and Information Sciences, Medicine and Health Sciences, Clinical Medicine, Diagnostic Medicine, Neurology, Research and Analysis Methods, Computational Techniques, Imaging Techniques, Medicine, R, Science, Q
Funded by
  • Funder: National Institutes of Health (NIH)
  • Project Code: 3P30AG010129-11S1
WT| King's College London Medical Engineering Centre.
  • Funder: Wellcome Trust (WT)
  • Project Code: 088641
  • Funding stream: Innovations
NIH| Alzheimers Disease Neuroimaging Initiative
  • Funder: National Institutes of Health (NIH)
  • Project Code: 1U01AG024904-01
  • Funder: Canadian Institutes of Health Research (CIHR)
Novel Methods leading to New Medications in Depression and Schizophrenia
  • Funder: European Commission (EC)
  • Project Code: 115008
  • Funding stream: FP7 | SP1 | SP1-JTI
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