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Generating knowledge for the identification of device failure causes and the prediction of the times-to-failure in u-Healthcare environments

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Abstract

The healthcare industry depends on a large number of medical devices to perform many of its functions, so a considerable amount of effort is spent to deal with failures occurred in medical devices. This paper proposes a method that generates knowledge used to identify the causes of medical device failures and to predict the times-to-failure (i.e., a period during which a medical device operates without failure). To generate knowledge for failure cause identification, morphemes of the failure data in the existing database are analyzed and similar failures (symptoms and causes) are grouped based on the similarity of symptoms. To generate knowledge for the prediction of the times-to-failure, the Weibull distribution parameters are estimated based on a device’s previous failure dates. The experiment results show that the proposed method has 69 % accuracy in identifying the cause of failure and 83 % accuracy in predicting the times-to-failure. The proposed method enables medical device users to quickly identify the cause of failure when their devices have problems, thereby reducing the cost of failure. With the predicted time to failure, it is possible to have devices (or device parts) ready just in time for replacement. This leads to decreased inventory costs.

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Notes

  1. Data published by the Ministry of Health and Welfare, South Korea.

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Authors and Affiliations

  1. School of Information Communication and Broadcasting Engineering, Halla University, Wonju, Korea

    Dong Woo Ryu

  2. School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea

    Kyung Jin Kang & Sang Oh Park

  3. Division of Computer Engineering, Mokwon University, Taejon, Korea

    Sang Soo Yeo

Authors
  1. Dong Woo Ryu
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  2. Kyung Jin Kang
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  3. Sang Soo Yeo
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  4. Sang Oh Park
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Corresponding author

Correspondence to Sang Oh Park.

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Ryu, D.W., Kang, K.J., Yeo, S.S. et al. Generating knowledge for the identification of device failure causes and the prediction of the times-to-failure in u-Healthcare environments. Pers Ubiquit Comput 17, 1383–1394 (2013). https://doi.org/10.1007/s00779-012-0573-7

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  • DOI: https://doi.org/10.1007/s00779-012-0573-7

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