Abstract
In this paper we present a general medical diagnostic expert system intended to serve as an educational self-diagnostic tool, openly available through the WWW. The system has been designed as an alternative to the common self-diagnosis practice among the general public of searching the Internet, finding the first disease with some matching symptoms, and treating this as a diagnosis, in contrast with the differential diagnosis offered by our system. We discuss the medical knowledge elicitation process, automated generation of Bayesian network models, and the diagnostic process. The system uses a scalable and efficient distributed reasoning engine based on multiple Bayesian networks. An analysis of over 100,000 diagnostic cases is presented. The cases are analyzed based on population characteristics such as age and gender. The results show the need for medical education and highlight the most common problems in non-emergency medical care.
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Zagorecki, A., Orzechowski, P., Hołownia, K. (2013). Online Diagnostic System Based on Bayesian Networks. In: Peek, N., Marín Morales, R., Peleg, M. (eds) Artificial Intelligence in Medicine. AIME 2013. Lecture Notes in Computer Science(), vol 7885. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38326-7_22
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DOI: https://doi.org/10.1007/978-3-642-38326-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38325-0
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