Annals of emergency medicine
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Multicenter Study Comparative Study
Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation.
Clinical and ECG data from presentation are highly discriminatory for diagnosis of acute coronary syndromes, whereas definitive diagnosis from serial ECG and cardiac marker protein measurements is usually not available for several hours. Artificial neural networks are computer programs adept at pattern recognition tasks and have been used to analyze data from chest pain patients with a view to developing diagnostic algorithms that might improve triage practices in the emergency department. The aim of this study is to develop and optimize artificial neural network models for diagnosis of acute coronary syndrome, to test these models on data collected prospectively from different centers, and to establish whether the performance of these models was superior to that of models derived using a standard statistical technique, logistic regression. ⋯ This study confirms that artificial neural networks can offer a useful approach for developing diagnostic algorithms for chest pain patients; however, the exceptional performance and simplicity of the logistic model militates in favor of logistic regression for the present task. Our artificial neural network models were well calibrated and performed well on unseen data from different centers. These issues have not been addressed in previous studies. However, and unlike in previous studies, we did not find the performance of artificial neural network models to be significantly different from that of suitably optimized logistic regression models.
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The accurate diagnosis of influenza remains a diagnostic dilemma. We examine the performance of various strategies for diagnosing influenza infection in an unselected sample of adults during influenza season. ⋯ The suggestion that a clinical decision rule or a rapid influenza test is better than clinical judgment alone for the diagnosis of influenza in an unselected patient population is not supported by this study.