Summary
This paper proposes risk mining, where data mining techniques were applied to detection and analysis of risks potentially existing in the organizations and to usage of risk information for better organizational management. We applied this technique to the following two medical domains: risk aversion of nurse incidents and infection control. The results show that data mining methods were effective to detection of risk factors.
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© 2008 Springer-Verlag Berlin Heidelberg
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Tsumoto, S., Matsuoka, K., Yokoyama, S. (2008). Risk Mining for Infection Control. In: Iwata, S., Ohsawa, Y., Tsumoto, S., Zhong, N., Shi, Y., Magnani, L. (eds) Communications and Discoveries from Multidisciplinary Data. Studies in Computational Intelligence, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78733-4_17
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DOI: https://doi.org/10.1007/978-3-540-78733-4_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78732-7
Online ISBN: 978-3-540-78733-4
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