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Evaluation of Chest Pain in the Emergency Department
The Editors welcome submissions for possible publication in the Letters section. Authors of letters should:
•Include no more than 300 words of text, three authors, and five references
•Type with double-spacing
•Send three copies of the letter, an authors' form signed by all authors, and a cover letter describing any conflicts of interest related to the contents of the letter.
Letters commenting on an Annals article will be considered if they are received within 6 weeks of the time the article was published. Only some of the letters received can be published. Published letters are edited and may be shortened; tables and figures are included only selectively. Authors will be notified that the letter has been received. If the letter is selected for publication, the author will be notified about 3 weeks before the publication date. Unpublished letters cannot be returned.
Annals welcomes electronically submitted letters.
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.
Frederic G. Jones
The Editors welcome submissions for possible publication in the Letters section. Authors of letters should:
•Include no more than 300 words of text, three authors, and five references
•Type with double-spacing
•Send three copies of the letter, an authors' form signed by all authors, and a cover letter describing any conflicts of interest related to the contents of the letter.
Letters commenting on an Annals article will be considered if they are received within 6 weeks of the time the article was published. Only some of the letters received can be published. Published letters are edited and may be shortened; tables and figures are included only selectively. Authors will be notified that the letter has been received. If the letter is selected for publication, the author will be notified about 3 weeks before the publication date. Unpublished letters cannot be returned.
Annals welcomes electronically submitted letters.
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