A clinical decision support system for aiding diagnosis of Alzheimer's disease and related disorders in mobile devices

The worldwide aging phenomenon is a growing concern. Alzheimer's disease (AD) has a high prevalence in the elderly. In this paper, we present a clinical decision support system for aiding the diagnosis of AD and related disorders. We describe system's main components and architecture, which is based on a mobile web-based platform. Its predictive model is based on Bayesian networks designed considering AD diagnosis criteria, trained and tested with the patient database of the Center for Alzheimer's Disease and Related Disorder at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil. Patient database attributes are composed by predisposal factors, demographic data, assessment scales, symptoms and signs. When the system indicates a patient diagnosis, it provides: the most probable diagnosis, health data that lead to such diagnosis and, in case of low certainty factor, unobserved health data that should be collected to confirm or refuse the initial diagnostic hypothesis. Preliminary usability tests indicate potential use of the system in clinical practice.