LETTER
Evaluation of Chest Pain in the Emergency Department
Frederic G. Jones
15 August 1995 | Volume 123 Issue 4 | Pages 314-318
TO THE EDITOR:
I read with interest the timely comments on the issues and possible solutions for optimizing assessment of patients presenting with suspected cardiac chest pain [1]. One of these approaches used decision-based computer models and probability analysis. Several references were listed, but surprisingly absent was the potential role for artificial neural networks in the decision-making process. A summary recently published in the September/October issue of American College of Cardiology Journal Review titled "Prognostication, Procrastination and Artificial Neural Networks." Many references were given in that review, including those to the work by Baxt that describes the use of the artificial neural network trained to identify the presence of myocardial infarction in patients presenting to emergency department [2, 3].
In addition, the 1994 Health Care Innovations in Technology Systems Partnership in Technology Award was awarded to an artificial intelligence system company working with the Florida Hospital in Orlando to address the same issues of identifying patients brought to the emergency department.
I hope you will bring this technology to the attention of your readers.
1. Kaul S, Abbott RD. Evaluation of chest pain in the emergency department (Editorial). Ann Intern Med. 1994; 121:976-8.
2. Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med. 1991; 115:845-8.
3. Baxt WG. Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction. Ann Emerg Med. 1992; 21:1439-44.
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