An Investigation into Healthcare-Data Patterns

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%.

The perils of artificial intelligence in healthcare: Disease diagnosis and treatment

“…Moreover, the vast majority of healthcare data centered on patients and their diseases are non-discrete, nonpatternable, and stochastic in nature (Boddy et al, 2019;JASON, 2017;Wang et al, 2015a), thereby making existing probabilistic statistical analysis useless. Consequently, even coupled with mountains of healthcare big data, AI"s critical decision-making around disease diagnosis and treatment is extraordinarily challenging, requiring a new AI algorithm to smartly deal with a multitude of non-patternable stochastic variables or factors associated with each individual disease, epidemic or rare.…”

Analysis of attribute‐based cryptographic techniques and their application to protect cloud services

“…These benefits encourage individual users and organizations to externalize their data storage to remote servers hosted by a cloud server provider (CSP). 1,2 The rapid transition toward cloud services raised many concerns in relation to the security of cloud data sharing activities. 3,4 In addition, data centers hosting cloud applications consume huge amounts of energy, mainly needed for processing huge amounts of data and enforcing suitable security mechanisms, thus contributing to high operational costs and carbon footprints to the environment.…”