Decision Support System Applied to Alzheimer's Disease Diagnosis

• Flávio L. Seixas UFF
• Bianca Zadrozny IBM Research Brasil
• Jerson Laks UFRJ
• Débora C. M. Saade UFF
• Aura Conci UFF

Abstract

This paper describes a clinical decision support system applied to Alzheimer’s disease diagnosis. Such system includes: a Bayesian knowledge base, an ontology defined to represent uncertainty inherent in clinical knowledge, an inference engine and a Bayesian learning algorithm. The Bayesian network structure was based on clinical Alzheimer criteria published by NINCDS-ADRDA and DSM-IV. The parameters were obtained using a Bayesian learning algorithm known as Expectation Maximization (EM). As a training database, we used clinical items from about 1500 cases available in the CERAD database. Results were evaluated using a sensitivity analysis.

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