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Bayesian Network-Based Model for the Diagnosis of Deterioration of Semantic Content Compatible with Alzheimer’s Disease

  • José María Guerrero Triviño
  • Rafael Martínez-Tomás
  • Herminia Peraita Adrados
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6686)

Abstract

Alzheimer’s Disease (AD) has become a serious public health problem that affects both the patient and his family and social environment, not to mention the high economic cost for families and public administrations. The early detection of AD has become one of the principal focuses of research, and its diagnosis is fundamental when the disease is incipient or even prodromic, because it is at these stages when treatments are more effective. There are numerous research studies to characterise the disease in these stages, and we have used the specific research carried out by Drs. Herminia Peraita and Lina Grasso. The application of Artificial Intelligence techniques, such as Bayesian Networks and Influence Diagrams, may provide a very valuable contribution both to the very research and the application of results. This article justifies using Bayesian Networks and Influence Diagrams to solve this type of problems and because of their great contribution to this application field. The modelling techniques used for constructing the Bayesian Network are mentioned in this article, and a mechanism for automatic learning of the model parameters is established.

Keywords

Bayesian Network Influence Diagram Corpus of Oral Definitions Naive Bayes Alzheimer’s Disease Cognitive Deterioration 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José María Guerrero Triviño
  • Rafael Martínez-Tomás
  • Herminia Peraita Adrados

There are no affiliations available

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