Neural Network Assessment of Perioperative Cardiac Risk in Vascular Surgery Patients

Pablo Lapuerta, Gilbert J. L'Italien, Sumita Paul, Robert C. Hendel, Jeffrey A. Leppo, Lee A. Fleisher, Mylan C. Cohen, Kim A. Eagle and Robert P. Giugliano

Medical Decision Making, 1998, vol. 18, issue 1, 70-75

Abstract: Neural networks were developed to predict perioperative cardiac complications with data from 567 vascular surgery patients. Neural network scores were based on cardiac risk factors and dipyridamole thallium results. These scores were converted into like lihood ratios that predicted cardiac risk. The prognostic accuracy of the neural networks was similar to that of logistic regression models (ROC areas 76.0% vs 75.8%), but their calibration was better. Logistic regression overestimated event rates in a group of high-risk patients (predicted event rate, 64%; observed rate 30%; n = 50, p

Date: 1998
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:18:y:1998:i:1:p:70-75

DOI: 10.1177/0272989X9801800114

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