Document Detail


Bayesian networks for cardiovascular monitoring.
MedLine Citation:
PMID:  17946804     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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:
Jennifer M Roberts; Tushar A Parlikar; Thomas Heldt; George C Verghese
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference     Volume:  1     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2006  
Date Detail:
Created Date:  2007-10-23     Completed Date:  2008-03-06     Revised Date:  2014-08-21    
Medline Journal Info:
Nlm Unique ID:  101243413     Medline TA:  Conf Proc IEEE Eng Med Biol Soc     Country:  United States    
Other Details:
Languages:  eng     Pagination:  205-9     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem
Cardiovascular Diseases / diagnosis*,  physiopathology*
Diagnosis, Computer-Assisted / methods*
Expert Systems*
Humans
Monitoring, Physiologic / methods*
Neural Networks (Computer)*
Pattern Recognition, Automated / methods*
Grant Support
ID/Acronym/Agency:
R01 EB001659/EB/NIBIB NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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