Risk stratification in heart failure using artificial neural networks

Proc AMIA Symp. 2000;32-6.

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.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

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