## 2 Background
### Machine Learning in EPR  
As electronic patient records are high-dimensional data sources, machine learning provides data analysts with the ability to automate data mining techniques [5]. As evidence of this, [PERSON] et al. outlined a system for the visualisation of EPR data by employing a systematic cluster analysis and dimension reduction approach. The focus of the work in [5] was towards the assistance of healthcare stakeholders, providing interactive visualisations of the data, which supports the administrative duties with five main applications including hypothesis generation, data exploration, data relationship identification, pattern recognition and results analysis. The research was comprehensive and had a clear patient benefit; however, it was different to that which is poised in this paper, where the emphasis is on security applications with and internally focused anomaly detection.  
With a focus on supporting text analysis processes when managing medical record data, [PERSON] et al. outlined the use of neural networks for text classification [6]. As outlined in their paper, medical text has a tendency to contain complex vocabulary, often with poor levels of grammar within the sentences. This can be additional challenge for autonomous machine-learning text-based applications. [PERSON] et al. proposed an adaption of the hierarchical attention neural network, for medical document interpretation. Their techniques are able to achieve upwards of 93.75% for an open inpatient medical records dataset (known as the _China Conference on Knowledge Graph and Semantic Computing_ dataset). With such successful results, it is clear that machine learning techniques have multitudinous benefits within the domain of text-based medical record analysis. However, again in [6], the focus was on supportive metrics rather than security applications.  
Within the EPR domain, a significant portion of research involving machine learning techniques has focused on supportive applications for doctors (with regards to medical condition detection). For example, [PERSON] et al. employed ensemble semi-supervised algorithms for the classification of chest X-rays for patients with tuberculosis [7]. The approach adopts a combination of predictions from three different Semi-Supervised Learning (SSL) algorithms (including co-training, self-training and tri-training). The experimentation was evaluated using standard measures of sensitivity, specificity and accuracy; where their algorithm outperformed other techniques four out of five occasions when a 30% labelled ratio was adopted. Again, focusing on supportive applications for medical applications, [PERSON] et al. considered the application of random decision trees for coronary artery disease identification [8]. Their approach makes use of a support vector machine, a decision tree of Chi-squared automatic interaction and a random tree. Again, using accuracy as a measure of success, the investigation yielded promising results of 96.70% using the random tree approach. As demonstrated, both projects presented the effectiveness of machine learning and advanced data analytics when applied to EPR data. For that reason, machine learning is considered a core part of the approach in this paper. However, with a security focus in mind, a consideration of the access control limitations should also be addressed.