A Knowledge-based Clinical Decision Support System for the diagnosis of Alzheimer Disease

Egileak: Eider Sanchez and Carlos Toro and Eduardo Carrasco and Patricia Bonachela and Carlos Parra and Gloria Bueno and Frank Guijarro

Data: 13.06.2011


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Abstract

Alzheimer Disease (AD) has become a major issue in developed countries due to medical advances that have extended the population longevity. Recent advances in early detection date the initial stages of AD several years before the first recognizable symptoms appear visible. While at present time, there has not been recognized a single cause for AD, the common approach to support the diagnosis is based on diagnostic image processing, psychological tests, neurological tests, etc. This method produces a large amount of data that has to be taken into account by the physicians when they perform their diagnosis. In this paper we present a Knowledge Engineering diagnosissupport tool for the detection of AD where ontologies and semantic reasoning play a fundamental role. Our work is intended to aid physicians in the early detection of AD by using multidisciplinary knowledge gathered, and inference and reasoning over the underlying Knowledge Bases. A test example of our tool is also shown and discussed.

BIB_text

@Article {
author = {Eider Sanchez and Carlos Toro and Eduardo Carrasco and Patricia Bonachela and Carlos Parra and Gloria Bueno and Frank Guijarro},
title = {A Knowledge-based Clinical Decision Support System for the diagnosis of Alzheimer Disease},
pages = {355-361},
keywds = {
Decision support systems, Computer aided diagnosis, Knowledge Based systems
}
abstract = {
Alzheimer Disease (AD) has become a major issue in developed countries due to medical advances that have extended the population longevity. Recent advances in early detection date the initial stages of AD several years before the first recognizable symptoms appear visible. While at present time, there has not been recognized a single cause for AD, the common approach to support the diagnosis is based on diagnostic image processing, psychological tests, neurological tests, etc. This method produces a large amount of data that has to be taken into account by the physicians when they perform their diagnosis. In this paper we present a Knowledge Engineering diagnosissupport tool for the detection of AD where ontologies and semantic reasoning play a fundamental role. Our work is intended to aid physicians in the early detection of AD by using multidisciplinary knowledge gathered, and inference and reasoning over the underlying Knowledge Bases. A test example of our tool is also shown and discussed.
}
isbn = {978-1-61284-696-5},
date = {2011-06-13},
year = {2011},
}
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