Myocardial Infarction Prediction by Artificial Neural Networks
Authors: Alan P. Zelicoff
Journal: Annals of Internal Medicine (1992)

Letters15 April 1992Myocardial Infarction Prediction by Artificial Neural NetworksAlan P. Zelicoff, MDAlan P. Zelicoff, MDSearch for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-116-8-701 SectionsAboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail ExcerptTo the Editors:Baxt's (1) successful use of neural networks in accurately diagnosing myocardial infarction in an emergency room setting illustrates the vast potential utility of this technique for medical decision making. Unfortunately, despite an apparently straightforward mathematical foundation, much of the design and training of neural networks remains an intuitive art. Network theory provides very little guidance for optimizing network structure, including optimizing the depth of hidden layers and the number of nodes in those layers, which are important determinants of computing resources necessary for training the network (2). Such considerations are paramount in large input nets and in...References1. Baxt W. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med. 1991;115:843-8. LinkGoogle Scholar2. McClellandRumelhard JD eds. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Cambridge, Massachusetts: MIT Press; 1986. Google Scholar1. Baxt W. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med. 1991;115:843-8. LinkGoogle Scholar2. Zar J. Biostatistical Analysis. 2d ed. 1984:378-9. Google Scholar1. Baxt W. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med. 1991;115:843-8. LinkGoogle Scholar2. Caudill M. Neural network primer, part III. AI Expert. 1988;3:53-9. Google Scholar3. Simpson P. Artificial Neural Systems: Foundations, Paradigms, Applications and Implementations. New York: Pergamon Press; 1990:85-94. Google Scholar1. Bailey B. Large sample simultaneous confidence intervals for the multinominal probabilities based on transformations of the cell frequencies. Technometrics. 1980;22:583-9. CrossrefGoogle Scholar2. ClopperPearson CE. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26:404. CrossrefGoogle Scholar3. Baxt W. Use of an artificial neural network for data analysis in clinical decision-making: the diagnosis of acute coronary occlusion. Neural Computation. 1991;2:480-9. CrossrefGoogle Scholar This content is PDF only. To continue reading please click on the PDF icon. Author, Article, and Disclosure InformationAuthors: Alan P. Zelicoff, MDAffiliations: Sandia National Laboratories-Division 9241 Albuquerque, NM 87185 PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetails Metrics Cited byA neural network approach to assessing the significance of input parameters for predicting the dynamics of an organism under the conditions of the conclusions of cardiac diseasesComparison of artificial neural networks with logistic regression in prediction of in-hospital death after percutaneous transluminal coronary angioplastyPerformance of a Neural Network Trained to Make Third-molar Treatment-planning DecisionsProposal for an editorial policy for publications describing the use of artificial neural networks in electrocardiography 15 April 1992Volume 116, Issue 8Page: 701-702KeywordsDecision makingEmergency departmentMyocardial infarctionNeural networks ePublished: 1 December 2008 Issue Published: 15 April 1992 PDF downloadLoading ...