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An Investigation into Healthcare-Data Patterns

Author

Listed:
  • Aaron Boddy

    (Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK)

  • William Hurst

    (Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK)

  • Michael Mackay

    (Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK)

  • Abdennour El Rhalibi

    (Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK)

  • Thar Baker

    (Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK)

  • Casimiro A. Curbelo Montañez

    (Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool L3 3AF, UK)

Abstract

Visualising complex data facilitates a more comprehensive stage for conveying knowledge. Within the medical data domain, there is an increasing requirement for valuable and accurate information. Patients need to be confident that their data is being stored safely and securely. As such, it is now becoming necessary to visualise data patterns and trends in real-time to identify erratic and anomalous network access behaviours. In this paper, an investigation into modelling data flow within healthcare infrastructures is presented; where a dataset from a Liverpool-based (UK) hospital is employed for the case study. Specifically, a visualisation of transmission control protocol (TCP) socket connections is put forward, as an investigation into the data complexity and user interaction events within healthcare networks. In addition, a filtering algorithm is proposed for noise reduction in the TCP dataset. Positive results from using this algorithm are apparent on visual inspection, where noise is reduced by up to 89.84%.

Suggested Citation

  • Aaron Boddy & William Hurst & Michael Mackay & Abdennour El Rhalibi & Thar Baker & Casimiro A. Curbelo Montañez, 2019. "An Investigation into Healthcare-Data Patterns," Future Internet, MDPI, vol. 11(2), pages 1-23, January.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:2:p:30-:d:201883
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