Document Detail


Long term cardiovascular risk models' combination - a new approach.
MedLine Citation:
PMID:  19964835     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This work addresses two major drawbacks of the current cardiovascular risk score systems: reduced number of risk factors considered by each individual tool and the inability of these tools to deal with incomplete information. To achieve this goal a two phase strategy was followed. In the first phase, a common representation procedure was considered, based on a Naïve-Bayes classifier methodology. Conditional probabilities parameters were initially evaluated through a frequency estimation method and after that optimized using a Genetic Algorithm approach. In a second phase, a combination scheme was proposed exploiting the particular features of Bayes probabilistic reasoning. This strategy was applied to describe and combine SCORE, ASSIGN and Framingham models. Validation results were obtained based on individual models, assuming their statistical correctness. The achieved results are very promising, showing the potential of the strategy to accomplish the desired goals.
Authors:
S Paredes; T Rocha; P de Carvalho; J Henriques; M Harris; J Morais
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
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:  2009     ISSN:  1557-170X     ISO Abbreviation:  Conf Proc IEEE Eng Med Biol Soc     Publication Date:  2009  
Date Detail:
Created Date:  2009-12-07     Completed Date:  2010-04-08     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:  4711-4     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Bayes Theorem*
Cardiovascular Diseases*
Humans
Models, Cardiovascular*
Reproducibility of Results
Risk Assessment
Risk Factors

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


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