Risk stratification in heart failure using artificial neural networks.

F. Atienza, N. Martinez-Alzamora, J. A. De Velasco, S. Dreiseitl, L. Ohno-Machado

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

25 Zitate (Scopus)


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.

Seiten (von - bis)32-36
FachzeitschriftProceedings / AMIA ... Annual Symposium. AMIA Symposium
PublikationsstatusVeröffentlicht - 2000
Extern publiziertJa


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