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5. Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction. Baxt WG. Ann Emerg Med; 1992 Dec 06; 21(12):1439-44. PubMed ID: 1443838 [Abstract] [Full Text] [Related]
6. A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain. Baxt WG, Shofer FS, Sites FD, Hollander JE. Ann Emerg Med; 2002 Dec 06; 40(6):575-83. PubMed ID: 12447333 [Abstract] [Full Text] [Related]
9. A decision tree for the early diagnosis of acute myocardial infarction in nontraumatic chest pain patients at hospital admission. Mair J, Smidt J, Lechleitner P, Dienstl F, Puschendorf B. Chest; 1995 Dec 06; 108(6):1502-9. PubMed ID: 7497751 [Abstract] [Full Text] [Related]
10. A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching. Baxt WG. Med Decis Making; 1994 Dec 06; 14(3):217-22. PubMed ID: 7934708 [Abstract] [Full Text] [Related]
11. Artificial neural network algorithms for early diagnosis of acute myocardial infarction and prediction of infarct size in chest pain patients. Eggers KM, Ellenius J, Dellborg M, Groth T, Oldgren J, Swahn E, Lindahl B. Int J Cardiol; 2007 Jan 18; 114(3):366-74. PubMed ID: 16797088 [Abstract] [Full Text] [Related]
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17. [Testing of a computer program model for the diagnosis of suspected acute coronary disease]. Jonsbu J, Aase O, Arnesen KE, Rollag A, Erikssen J. Tidsskr Nor Laegeforen; 1990 Mar 30; 110(9):1077-81. PubMed ID: 2184539 [Abstract] [Full Text] [Related]
18. Artificial neural networks for the electrocardiographic diagnosis of healed myocardial infarction. Hedén B, Edenbrandt L, Haisty WK, Pahlm O. Am J Cardiol; 1994 Jul 01; 74(1):5-8. PubMed ID: 8017306 [Abstract] [Full Text] [Related]