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This 40 page paper provides an overview of the use of artificial neural networks for interpretation of medical data. Bibliography lists 15 sources.
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40 pages (~225 words per page)
File: MH11_MHNeuEEG.rtf
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but if the student requires additional articles to be incorporated into this review of literature, they should make arrangements to forward copies of these articles to the office and request
that they be added to the text of the paper. Introduction Over the course of the past two decades, the focus on the use of artificial neural networks, including
those that have become the basis for decision support systems, have permeated many different industries. Though many industries have successfully integrated new and emerging technologies, the health care industry
has struggled with the process through which the full potential of artificial neural networks should be realized. In fact, researchers have attempted to determine the best areas within
which to apply artificial neural networks relative to diagnostic processes and data analysis while being cognizant of the very real human concerns about the use of artificial neural networks and
computer systems in general. With computer-based systems, including artificial intelligence and neural networks, existing as a major part of the emerging health care model, researchers have attempted to determine
the validity and reliability of outcomes related to the use of neural networks in assessing data that can have a direct impact on patient care. Research studies
have been conducted since the late 1980s that reflect the effort to integrate artificial intelligence, especially artificial neural networks, into models designed to predict health and medical outcomes. A
variety of different researchers, both in the United States and abroad, have studied the use of artificial neural networks to assess data and provide interpretive information in the curative environment.
Dr. Edward E. E. Frietman and C. J. Stam, in their study Use of Artificial Neural Networks for Automatic Description and Interpretation of Electroencephalogram (2004) provides a basis for
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