| Risk stratification in heart failure using artificial neural networks. | |
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MedLine Citation:
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PMID: 11079839 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Accurate risk stratification of heart failure patients is critical to improve management and outcomes. Heart failure is a complex multisystem disease in which several predictors are categorical. Neural network models have successfully been applied to several medical classification problems. Using a simple neural network, we assessed one-year prognosis in 132 patients, consecutively admitted with heart failure, by classifying them in 3 groups: death, readmission and one-year event-free survival. Given the small number of cases, the neural network model was trained using a resampling method. We identified relevant predictors using the Automatic Relevance Determination (ARD) method, and estimated their mean effect on the 3 different outcomes. Only 9 individuals were misclassified. Neural networks have the potential to be a useful tool for making prognosis in the domain of heart failure. |
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Authors:
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F Atienza; N Martinez-Alzamora; J A De Velasco; S Dreiseitl; L Ohno-Machado |
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Publication Detail:
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Type: Evaluation Studies; Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S. |
Journal Detail:
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Title: Proceedings / AMIA ... Annual Symposium. AMIA Symposium Volume: - ISSN: 1531-605X ISO Abbreviation: Proc AMIA Symp Publication Date: 2000 |
Date Detail:
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Created Date: 2001-01-10 Completed Date: 2001-03-08 Revised Date: 2012-10-09 |
Medline Journal Info:
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Nlm Unique ID: 100883449 Medline TA: Proc AMIA Symp Country: United States |
Other Details:
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Languages: eng Pagination: 32-6 Citation Subset: IM |
Affiliation:
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Cardiology Department, University General Hospital, Valencia, Spain. fatienzaf@meditex.es |
Export Citation:
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| MeSH Terms | |
Descriptor/Qualifier:
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Disease-Free Survival Heart Failure / classification*, mortality Humans Neural Networks (Computer)* Patient Readmission Prognosis Risk Assessment / methods Sensitivity and Specificity |
| Grant Support | |
ID/Acronym/Agency:
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LM/OD06538-01/LM/NLM NIH HHS; R01 LM006538/LM/NLM NIH HHS |
| Comments/Corrections | |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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