Name
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 not-work, 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. |
Year | Venue | Field |
|---|---|---|
2000 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | Medical classification,Heart failure,Disease,Categorical variable,Risk assessment,Intensive care medicine,Artificial neural network,Resampling,Medicine |
DocType | Issue | ISSN |
Conference | SUPnan | 1067-5027 |
Citations | PageRank | References |
7 | 0.74 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Felipe Atienza | 1 | 12 | 8.15 |
| N Martinez-Alzamora | 2 | 7 | 0.74 |
| J A De Velasco | 3 | 7 | 0.74 |
| Stephan Dreiseitl | 4 | 338 | 34.80 |
| Lucila Ohno-Machado | 5 | 1426 | 187.95 |