Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer's disease

Mark Van Gils, Juha Koikkalainen, Jussi Mattila, Sanna Kaisa Herukka, Jyrki Lötjönen, Hilkka Soininen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

10 Citations (Scopus)

Abstract

Objective and early detection of Alzheimer's disease (AD) is a demanding problem requiring consideration of manymodal observations. Potentially, many features could be used to discern between people without AD and those at different stages of the disease. Such features include results from cognitive and memory tests, imaging (MRI, PET) results, cerebral spine fluid data, blood markers etc. However, in order to define an efficient and limited set of features that can be employed in classifiers requires mining of data from many patient cases. In this study we used two databases, ADNI and Kuopio LMCI, to investigate the relative importance of features and their combinations. Optimal feature combinations are to be used in a Clinical Decision Support System that is to be used in clinical AD diagnosis practice.

Original languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Pages2886-2889
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Publication typeA4 Article in conference proceedings
Event2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10 - Buenos Aires, Argentina
Duration: 31 Aug 20104 Sept 2010

Publication series

Name2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Conference

Conference2010 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10
Country/TerritoryArgentina
CityBuenos Aires
Period31/08/104/09/10

ASJC Scopus subject areas

  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Health Informatics

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