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Extracting Propagation Patterns from Bacterial Culture Data in Medical Facility

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New Frontiers in Artificial Intelligence (JSAI-isAI 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10091))

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Abstract

In this paper, we formulate propagation patterns as the pairs of records in the same bacterial culture occurring within a fixed span in bacterial culture data. Then, we design the exhaustive search algorithm to extract all of the propagation patterns from bacterial culture data based on the extended principle of the 2-dimensional career map to determine whether two records in bacterial culture data belong to the same bacterial culture or the different ones. In particular, we focus on infectious propagation patterns, in which two patients are not identical, and they are in the same room and/or treated by the same physician. Finally, we give the experimental results to extract all of the propagation patterns and analyze them.

This work is partially supported by Grand-in-Aid for Scientific Research 24300060, 24240021, 26280085 and 15K12102 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.

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Acknowledgment

The authors would like to thank anonymous referees of TSDAA 2015 for valuable comments to revise the previous version.

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Authors and Affiliations

  1. Department of Artificial Intelligence, Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Kazuki Nagayama

  2. KD-ICONS Co. Ltd., Ohmori-minami 4-6-15-303, Ohta, Tokyo, 143-0013, Japan

    Shigeki Yokoyama

  3. Osaka Prefectural General Medical Center, Bandai-higashi 3-1-56, Sumiyoshi, Osaka, 558-8558, Japan

    Kimiko Matsuoka

  4. Department of Artificial Intelligence, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Kouichi Hirata

Authors
  1. Kazuki Nagayama
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  2. Kouichi Hirata
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  3. Shigeki Yokoyama
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  4. Kimiko Matsuoka
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Corresponding author

Correspondence to Kouichi Hirata .

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Editors and Affiliations

  1. Chiba University Graduate School of Engineering, Chiba, Japan

    Mihoko Otake

  2. University of Tsukuba, Tokyo, Japan

    Setsuya Kurahashi

  3. Fujitsu Laboratories, Kanagawa, Japan

    Yuiko Ota

  4. National Institute of Informatics, Tokyo, Japan

    Ken Satoh

  5. Ochanomizu University, Tokyo, Japan

    Daisuke Bekki

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Nagayama, K., Hirata, K., Yokoyama, S., Matsuoka, K. (2017). Extracting Propagation Patterns from Bacterial Culture Data in Medical Facility. In: Otake, M., Kurahashi, S., Ota, Y., Satoh, K., Bekki, D. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2015. Lecture Notes in Computer Science(), vol 10091. Springer, Cham. https://doi.org/10.1007/978-3-319-50953-2_28

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  • DOI: https://doi.org/10.1007/978-3-319-50953-2_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50952-5

  • Online ISBN: 978-3-319-50953-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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