Abstract
The process of clinical diagnosis poses a complex problem, it is the first step of any process in health care. Acute coronary syndromes are one of the main causes of death worldwide; its most frequent presentation, acute chest pain, requires for diagnosis, take into consideration aspects of the clinical record, the electrocardiogram, and markers of heart disease. Intelligent systems tools have been successfully tested for the diagnosis. Multi-agent systems are a promising way for the computer modeling of this process. In theory, relevant data can be gathered and adaptability and learning capabilities can be added. The present work presents a federation of agents which are the product of an analysis made through the AOPOA methodology, which integrates the diagnosis of chest pain focused on acute coronary syndromes by means of a neural network assembly system (some of the neural networks are specialized in special populations), exhibiting a high level of diagnostic accuracy.
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Acknowledgments
The authors are indebted to the members of the Department of Research and Statistics and to the medical staff and residents of the internal medicine service of the Fundación Universitaria Ciencias de la Salud—Hospital San José of Bogotá.
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Sprockel, J.J., Gonzalez, E. (2015). Assembly of Neural Networks Within a Federation of Rational Agents for the Diagnosis of Acute Coronary Syndromes. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXII. SGAI 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-25032-8_22
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DOI: https://doi.org/10.1007/978-3-319-25032-8_22
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