Journal article
Cardiac Risk Stratification in Renal Transplantation Using a Form of Artificial Intelligence
American Journal of Cardiology, Vol.79, pp.415-417
1997
Abstract
The purpose of this study was to determine if an expert network, a form of artificial intelligence, could effectively stratify cardiac risk in candidates for renal transplant. Input into the expert network consisted of clinical risk factors and thallium-201 stress test data. Clinical risk factor screening alone identified 95 of 189 patients as high risk. These 95 patients underwent thallium-201 stress testing, and 53 had either reversible or fixed defects. The other 42 patients were classified as low risk. This algorithm made up the ''expert system,'' and during the 4-year follow-up period had a sensitivity of 82%, specificity of 77%, and accuracy of 78%. An artificial neural network was added to the expert system, creating an expert network. Input into the neural network consisted of both clinical variables and thallium-201 stress test data. There were 5 hidden nodes and the output (end point) was cardiac death. The expert network increased the specificity of the expert system alone from 77% to 90% (p õ0.001), the accuracy from 78% to 89% (p õ0.005), and maintained the overall sensitivity at 88%. An expert network based on clinical risk factor screening and thallium-201 stress testing had an accuracy of 89% in predicting the 4-year cardiac mortality among 189 renal transplant candidates. 1997 by
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Details
- Title
- Cardiac Risk Stratification in Renal Transplantation Using a Form of Artificial Intelligence
- Creators
- Thomas F. Heston (Author) - Washington State University, Elson S. Floyd College of Medicine
- Publication Details
- American Journal of Cardiology, Vol.79, pp.415-417
- Academic Unit
- Elson S. Floyd College of Medicine
- Identifiers
- 99901107736401842
- Language
- English
- Resource Type
- Journal article