Risk stratification in heart failure using artificial neural networks.

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

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)32-36
Number of pages5
JournalProceedings / AMIA ... Annual Symposium. AMIA Symposium
Publication statusPublished - 2000
Externally publishedYes

Keywords

  • Disease-Free Survival
  • Heart Failure/classification
  • Humans
  • Neural Networks, Computer
  • Patient Readmission
  • Prognosis
  • Risk Assessment/methods
  • Sensitivity and Specificity

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