title: Bayesian networks for cardiovascular monitoring.
abstract: Bayesian Networks provide a flexible way of incorporating different types of information into a single probabilistic model. In a medical setting, one can use these networks to create a patient model that incorporates lab test results, clinician observations, vital signs, and other forms of patient data. In this paper, we explore a simple Bayesian Network model of the cardiovascular system and evaluate its ability to predict unobservable variables using both real and simulated patient data.
authors.affiliation: Dept. of Electrical Engineering & Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
authors.forename: Jennifer M
authors.initials: JM
authors.lastname: Roberts
authors.forename: Tushar A
authors.initials: TA
authors.lastname: Parlikar
authors.forename: Thomas
authors.initials: T
authors.lastname: Heldt
authors.forename: George C
authors.initials: GC
authors.lastname: Verghese
mesh_terms: D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005103:Expert Systems; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*