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.
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Arias-Calleja, M.: Carmen: una herramienta de software librepara modelos gráficos probabilistas (2009)
Bottcher, S.G., Dethlefsen, C.: Learning bayesian networks with r. In: International Workshop on Distributed Statistical Computing, DSC 2003 (2003)
Cree, G.S., McRae, K.: Analyzing the factors underlying the structure and computation of the meaning of chipmunk, cherry, chisel, cheese, and cello (and many other such concrete nouns). Journal of Experimental Psychology: General 132, 163–201 (2003)
Díez-Vegas, F.J.: Teoría probabilista de la decisión en medicina (2007)
Fernández-Galán, S., Díez-Vegas, F.J.: Modelling Dynamic Causal Interactions with BayesianNetworks: Temporal Noisy Gates (2000)
Kjaerulff, U.B., Madsen, A.L.: Bayesian Networks and Influence Diagrams
Lacave, C.: Explicación en Redes Bayesianas (2002)
McRae, K., Cree, G.S., Seidenberg, M.S., McNorman, C.: Semantic feature production norms for a large set of living and non living things. Behaviour Research Methods 37, 547–559 (2005)
Moreno, F.J., Peraita, H.: Análisis de la estructura conceptual de categories semánticas naturales y artificiales en una muestra de pacientes de alzheimer. Psicothema 18(3), 492–500 (2006)
Neapolitan, R.E.: Learning Bayesian Networks. Series in Artificial Intelligence. Prentice-Hall, Englewood Cliffs (2004)
Nielson, T.D.: Bayesian Networks and Decision Graphs (2007)
Peraita, H.: Corpus lingüístico de definiciones de categorías semánticas de personas mayores sanas y con la enfermedad del alzheimer. Technical report, Departamento De Psicología Básica 1. Facultad de Psicología. UNED (2009)
Peraita, H., Galeote, M.Á., González-Labra, M.J.: Deterioro dela memoria semántica en pacientes de alzheimer. Psicothema 11(4), 917–937 (1999)
Peraita, H., Grasso, L., Mardomingo, M.C.: Análisis preliminar de rasgos de definiciones de categorías semánticas del corpus lingüístico de sujetos sanos y con enfermedad de alzheimer, Technical report, Departamento de Psicología Básica 1. Facultad de Psicología. UNED (2009)
Valls-Pedret, C.: Diagnóstico precoz de la enfermedad de alzheimer: fase prodrómica y preclínica. Rev. Neurol. 51(8), 471–480 (2010)
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Guerrero Triviño, J.M., Martínez-Tomás, R., Peraita Adrados, H. (2011). Bayesian Network-Based Model for the Diagnosis of Deterioration of Semantic Content Compatible with Alzheimer’s Disease. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) Foundations on Natural and Artificial Computation. IWINAC 2011. Lecture Notes in Computer Science, vol 6686. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21344-1_44
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DOI: https://doi.org/10.1007/978-3-642-21344-1_44
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