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Mining Medical Periodic Patterns from Spatio-Temporal Trajectories

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Health Information Science (HIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11148))

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Abstract

A spatio-temporal trajectory captures the movement behaviors of an object, and reveals various periodic patterns for the object such as where and when the object regularly visits. Due to the recent advances in GPS-enabled data collection devices such as mobile phones, a large set of spatio-temporal trajectories has been collected and available for analysis. These spatio-temporal trajectories could be used to identify those people who periodically visit medical centres for treatments (patients), working (health professionals) or other purposes. Spatio-temporal periodic pattern mining is to find periodic patterns for a certain place at regular intervals from spatio-temporal trajectories. Past studies attempt to find periodic patterns in medical contexts through time-series datasets, but not from spatio-temporal trajectories. In this study, we introduce a medical periodic pattern mining framework that utilises spatio-temporal periodic pattern mining approaches to find medical periodic patterns. We test the feasibility and applicability of our framework through a real-world publicly available dataset. Experimental results reveal that our framework is able to identify those people who regularly visit medical centres from those not, and also find medical periodic patterns revealing interesting medical behaviors.

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Notes

  1. 1.

    https://privamov.github.io/accio/reference/datasets/.

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Authors and Affiliations

  1. Discipline of Computer Science and Information Technology, College of Science and Engineering, James Cook University, PO Box 6811, Cairns, QLD, 4870, Australia

    Dongzhi Zhang, Kyungmi Lee & Ickjai Lee

Authors
  1. Dongzhi Zhang
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  2. Kyungmi Lee
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  3. Ickjai Lee
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Corresponding author

Correspondence to Ickjai Lee .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. James Cook University, Cairns, QLD, Australia

    Ickjai Lee

  3. Vrije University of Amsterdam, Amsterdam, The Netherlands

    Zhisheng Huang

  4. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

  5. Victoria University, Footscray, VIC, Australia

    Hua Wang

  6. James Cook University, Cairns, QLD, Australia

    Wei Xiang

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Zhang, D., Lee, K., Lee, I. (2018). Mining Medical Periodic Patterns from Spatio-Temporal Trajectories. In: Siuly, S., Lee, I., Huang, Z., Zhou, R., Wang, H., Xiang, W. (eds) Health Information Science. HIS 2018. Lecture Notes in Computer Science(), vol 11148. Springer, Cham. https://doi.org/10.1007/978-3-030-01078-2_11

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  • DOI: https://doi.org/10.1007/978-3-030-01078-2_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01077-5

  • Online ISBN: 978-3-030-01078-2

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