A hierarchical, ontology-driven Bayesian concept for ubiquitous medical environments--a case study for pulmonary diseases.
The present paper extends work on an existing computer-based Decision Support System (DSS) that aims to provide assistance to physicians as regards to pulmonary diseases. The extension deals with allowing for a hierarchical decomposition of the task, at different levels of domain granularity, using a novel approach, i.e. Hierarchical Bayesian Networks. The proposed framework uses data from various networking appliances such as mobile phones and wireless medical sensors to establish a ubiquitous environment for medical treatment of pulmonary diseases. Domain knowledge is encoded at the upper levels of the hierarchy, thus making the process of generalization easier to accomplish. The experimental results were carried out under the Pulmonary Department, University Regional Hospital Patras, Patras, Greece. They have supported our initial beliefs about the ability of Bayesian networks to provide an effective, yet semantically-oriented, means of prognosis and reasoning under conditions of uncertainty.
Maragoudakis
Manolis
M
Department of Information and Communication Systems Engineering, Artificial Intelligence Laboratory, University of Aegean, Greece. mmarag@aegean.gr
Lymberopoulos
Dimitrios
D
Fakotakis
Nikos
N
Spiropoulos
Kostas
K
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003198:Computer Simulation; D003199:Computer Systems; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis* / Q000473:pathology; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D018670:Monitoring, Ambulatory / Q000379:methods; D011379:Prognosis; D015203:Reproducibility of Results; D014584:User-Computer Interface