Density-Based Outlier Detection for Safeguarding Electronic Patient Record Systems
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 956
- Type
- D - Journal article
- DOI
-
10.1109/ACCESS.2019.2906503
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 40285
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 8
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work is the culmination of a four-year research collaboration with Aintree University Hospital into the detection of abnormal data usage and unauthorized access in healthcare infrastructures. With over 83% of hospitals adopting Electronic Patient Records, this research is timely and impactful. To support the research, Aintree Hospital provided a financial contribution and over 1 million rows of data (Aaron Boddy, Business Analyst, aaronboddy@hotmail.co.uk). Access to this type of data is both rare and unique. As such the work within the paper is novel and has the advantage of being testing on real-world data for validation of the results.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
