STUDY OBJECTIVE: Accurate identification of the presence of acute myocardial infarction in adult patients who present to the emergency department with anterior chest pain remains elusive. The artificial neural network is a powerful nonlinear statistical paradigm for the recognition of complex patterns, with the ability to maintain accuracy when some data required for network function are missing. Earlier studies revealed that the artificial neural network is able to accurately identify acute myocardial infarction in patients experiencing chest pain. However, these studies did not measure network performance in real time, when a significant amount of data required for network function may not be available. They also did not use chemical cardiac marker data. METHODS: Two thousand two hundred four adult patients presenting to the ED with anterior chest pain were used to train an artificial neural network to recognize the presence of acute myocardial infarction. Only data available at the time of initial patient evaluation were used to replicate the conditions of real-time patient evaluation. Forty variables from patient histories, physical examinations, ECG results, and chemical cardiac marker determinations were used to train and then test the network. RESULTS: The network correctly identified 121 of the 128 patients (sensitivity 94.5%; 95% confidence interval 90.6% to 97.9%) with myocardial infarction at a specificity of 95.9% (95% confidence interval 93.0% to 98.5%), despite the fact that an average of 5% (individual range 0% to 35%) of the input data required by the network were missing on all patients. CONCLUSION: Network accuracy and the maintenance of that accuracy when some data required for function are unavailable suggest that the artificial neural network may be a potential real time aid to the diagnosis of acute myocardial infarction during initial patient evaluation.
STUDY OBJECTIVE: Accurate identification of the presence of acute myocardial infarction in adult patients who present to the emergency department with anterior chest pain remains elusive. The artificial neural network is a powerful nonlinear statistical paradigm for the recognition of complex patterns, with the ability to maintain accuracy when some data required for network function are missing. Earlier studies revealed that the artificial neural network is able to accurately identify acute myocardial infarction in patients experiencing chest pain. However, these studies did not measure network performance in real time, when a significant amount of data required for network function may not be available. They also did not use chemical cardiac marker data. METHODS: Two thousand two hundred four adult patients presenting to the ED with anterior chest pain were used to train an artificial neural network to recognize the presence of acute myocardial infarction. Only data available at the time of initial patient evaluation were used to replicate the conditions of real-time patient evaluation. Forty variables from patient histories, physical examinations, ECG results, and chemical cardiac marker determinations were used to train and then test the network. RESULTS: The network correctly identified 121 of the 128 patients (sensitivity 94.5%; 95% confidence interval 90.6% to 97.9%) with myocardial infarction at a specificity of 95.9% (95% confidence interval 93.0% to 98.5%), despite the fact that an average of 5% (individual range 0% to 35%) of the input data required by the network were missing on all patients. CONCLUSION: Network accuracy and the maintenance of that accuracy when some data required for function are unavailable suggest that the artificial neural network may be a potential real time aid to the diagnosis of acute myocardial infarction during initial patient evaluation.
Authors: Erik P Hess; Dipti Agarwal; Subhash Chandra; Mohammed H Murad; Patricia J Erwin; Judd E Hollander; Victor M Montori; Ian G Stiell Journal: CMAJ Date: 2010-06-07 Impact factor: 8.262
Authors: Marco V Perez; Frederick E Dewey; Swee Y Tan; Jonathan Myers; Victor F Froelicher Journal: Ann Noninvasive Electrocardiol Date: 2009-01 Impact factor: 1.468