Multi-level medical periodic patterns from human movement behaviors

  • Dongzhi Zhang
  • Kyungmi Lee
  • Ickjai LeeEmail author
Part of the following topical collections:
  1. Special Issue on Application of Artificial Intelligence in Health Research


Human movement behaviors could reveal many interesting medical patterns. Due to the advances in location-aware devices, a large volume of human movement behaviors has been captured in the form of spatio-temporal trajectories. These spatio-temporal trajectories are useful resources for medical data mining, and they could be used to classify which trajectory passes through medical centres and which one does not. Traditional approaches utilise time-series datasets while ignoring spatio-temporal semantics in order to detect periodic patterns in medical domains. They also fail to consider the inherent hierarchical nature of patterns. We investigate a medical data mining framework that generates multi-level medical periodic patterns. A Geolife dataset is used to test the feasibility and applicability of our framework. Experiments demonstrate that the proposed framework successfully distinguishes those who periodically visit medical centres from those who do not, and also to find multi-level medical periodic patterns revealing interesting hierarchical medical behaviours. One potential application includes an automated personalised medical service. For instance, medical institutions can send personalised relative medicine information to people who regularly visit certain medical centres. It will be useful for the discovery and diagnosis of diseases for patients.


Periodic pattern mining Multi-level hierarchical patterns Spatio-temporal trajectories Medical patterns 


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Computer Science & Information Technology Academy, Division of Tropical Environments & SocietiesJames Cook UniversityCairnsAustralia

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