NDLI: Risk assessment for acute myocardial infarction patients using artificial neural networks

Content Provider	IEEE Xplore Digital Library
Author	Sepulveda, J. Soria, E. Camps, G. Sanz, G. Marrugat, J. Gomez, L.
Copyright Year	2001
Description	Author affiliation: Valencia Univ., Spain (Sepulveda, J.)
Abstract	The purpose of the study was to develop a clinical score for risk assessment to determine the profile of every patient with acute myocardial infarction (MI). A cohort of 1,318 consecutive patients with a first MI admitted to four referral teaching hospitals (one with tertiary facilities) were followed up for 6 months after admission. To classify patients an artificial neural network (ANN), called a multilayer perceptron was used with the backpropagation learning algorithm. This method is used to analyse a collection of simple clinical markers. The model has achieved high values of sensitivity and specificity in the classification of the patients, training both in the training cohort (91.58% sensitivity, 79.37% specificity), and the validation cohort (88.46% sensitivity, 78.09% specificity). The use of simple clinical variables allows ANNs to give a reliable prediction of risk for in-hospital and 6 month mortality.
Starting Page	573
Ending Page	575
File Size	271266
Page Count	3
File Format	PDF
ISBN	0780372662
ISSN	02766547
DOI	10.1109/CIC.2001.977720
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-09-23
Publisher Place	Netherlands
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Risk management Myocardium Artificial neural networks Hospitals Angiography Education Biochemistry Medical treatment Surgery Multilayer perceptrons
Content Type	Text
Resource Type	Article

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