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NLM109954017 |
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DE-627 |
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20231222152438.0 |
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231222s2000 xx ||||| 00| ||eng c |
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|a pubmed24n0367.xml
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|a (DE-627)NLM109954017
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|a (NLM)11079839
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Atienza, F
|e verfasserin
|4 aut
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|a Risk stratification in heart failure using artificial neural networks
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|c 2000
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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| 500 |
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|a Date Completed 08.03.2001
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|a Date Revised 10.12.2019
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a 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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|a Evaluation Study
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 650 |
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|a Research Support, U.S. Gov't, P.H.S.
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| 700 |
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|a Martinez-Alzamora, N
|e verfasserin
|4 aut
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| 700 |
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|a De Velasco, J A
|e verfasserin
|4 aut
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| 700 |
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|a Dreiseitl, S
|e verfasserin
|4 aut
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| 700 |
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|a Ohno-Machado, L
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Proceedings. AMIA Symposium
|d 1998
|g (2000) vom: 01., Seite 32-6
|w (DE-627)NLM098642928
|x 1531-605X
|7 nnns
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|a GBV_ILN_350
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|a AR
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|j 2000
|b 01
|h 32-6
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