NDLI: Tendency discovery from incident report map generated by self organizing map and its development

Content Provider	IEEE Xplore Digital Library
Author	Kawanaka, H. Otani, Y. Yamamoto, K. Shinogi, T. Tsuruoka, S.
Copyright Year	2007
Description	Author affiliation: Mie Univ., Tsu (Kawanaka, H.; Otani, Y.)
Abstract	This study discusses a tendency discovery method from medical text data with free format. In this study, we focuses incident reports and proposes a new tendency discovery method for them using self-organizing map. In the first of this paper, we describes the outline of the keyword extraction method from incident reports and coding method to make a map. It is expected that the generated map by the proposed method shows the relation among the incident reports visually and it gives us new knowledge, i.e. trends or features underlaid in reports and difficult to find from each report. The knowledge will be able to help the reduction of incidents in the hospital. Moreover, authors developed a incident report analysis system using the proposed method and discuss the effectiveness of the proposed system. Finally, the paper describes a possibility for the application of SOM to incident reports in the end of this paper.
Starting Page	2016
Ending Page	2021
File Size	877439
Page Count	6
File Format	PDF
ISBN	9781424409907
DOI	10.1109/ICSMC.2007.4414011
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-10-07
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Organizing Medical diagnostic imaging Data mining Hospitals Clinical diagnosis Medical tests Application software Information technology Computational Intelligence Society Electronic medical prescriptions
Content Type	Text
Resource Type	Article

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