Knowledge-Based Discrimination in Alzheimer’s Disease View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Simon Duchesne , Burt Crépeault , Carol Hudon

ABSTRACT

Our goal is to propose a methodology to integrate clinical, cognitive, genetic and neuroimaging results, a disparate assembly of categorical, ordinal, and numerical data, within the context of Alzheimer’s disease (AD) research. We describe a knowledge-based, explicit decision model for the discrimination of AD subjects from normal controls (CTRL) based on a recent approach for diagnostic criteria for AD. We proceed by (a) creating a set of rules for each datum source; (b) integrating these disparate data into an information feature in the form of a binary string; and (c) using a machine learning approach based on the Hamming distance as an information similarity measure for the discrimination of probable AD from CTRL. When tested on 249 subjects from the multi-centric Alzheimer’s Disease Neuroimaging Initiative study, we reach up to 99.8% discriminative accuracy using a 9-bit information string in a series of 10-fold validation experiments. We expect this versatile approach to become useful in the identification of specific and sensitive phenotypes from large amounts of disparate data. More... »

PAGES

89-96

Book

TITLE

Medical Content-Based Retrieval for Clinical Decision Support

ISBN

978-3-642-11768-8
978-3-642-11769-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-11769-5_9

DOI

http://dx.doi.org/10.1007/978-3-642-11769-5_9

DIMENSIONS

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145 https://www.grid.ac/institutes/grid.420732.0 schema:alternateName Institut Universitaire en Santé Mentale de Quebec
146 schema:name Centre de Recherche Université Laval – Robert Giffard, Québec, QC, Canada
147 rdf:type schema:Organization
 




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