An Investigation into Healthcare-Data Patterns

Aaron Boddy, William Hurst, Michael Mackay, Abdennour El Rhalibi, Thar Baker, Casimiro Adays Curbelo Montañez

Research output: Contribution to journalArticlepeer-review

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%.
Original languageEnglish
Article number30
JournalFuture Internet
Volume11
Issue number2
DOIs
Publication statusPublished - 30 Jan 2019

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