W G Baxt1. 1. Department of Emergency Medicine, University of California, San Diego Medical Center.
Abstract
STUDY OBJECTIVE: To determine which clinical variables drive the output of an artificial neural network trained to identify the presence of myocardial infarction. DESIGN: Partial output analysis. SETTING: Tertiary university teaching center. PARTICIPANTS: Seven hundred six patients more than 18 years old presenting with anterior chest pain. MEASUREMENTS: Differential network output analysis. MAIN RESULTS: A methodology was developed as the first step in measuring the impact input clinical variables have on the output (diagnosis) of an artificial neural network trained to identify the presence of acute myocardial infarction. The methodology revealed that the network used the presence of ECG findings, as well as the presence of rales, syncope, jugular venous distension, response to trinitroglycerin, and nausea and vomiting, as major predictive sources. Although this first-step analysis studied individual variables, it must be stated that the network comes to clinical closure based on the settings of all variables in a pattern and that the impact of a single variable cannot be taken out of the context of a pattern. CONCLUSION: An artificial neural network trained to recognize the presence of myocardial infarction appears to place diagnostic importance on clinical variables that have not been shown previously to be highly predictive for infarction.
STUDY OBJECTIVE: To determine which clinical variables drive the output of an artificial neural network trained to identify the presence of myocardial infarction. DESIGN: Partial output analysis. SETTING: Tertiary university teaching center. PARTICIPANTS: Seven hundred six patients more than 18 years old presenting with anterior chest pain. MEASUREMENTS: Differential network output analysis. MAIN RESULTS: A methodology was developed as the first step in measuring the impact input clinical variables have on the output (diagnosis) of an artificial neural network trained to identify the presence of acute myocardial infarction. The methodology revealed that the network used the presence of ECG findings, as well as the presence of rales, syncope, jugular venous distension, response to trinitroglycerin, and nausea and vomiting, as major predictive sources. Although this first-step analysis studied individual variables, it must be stated that the network comes to clinical closure based on the settings of all variables in a pattern and that the impact of a single variable cannot be taken out of the context of a pattern. CONCLUSION: An artificial neural network trained to recognize the presence of myocardial infarction appears to place diagnostic importance on clinical variables that have not been shown previously to be highly predictive for infarction.