Improving predictive models of glaucoma severity by incorporating quality indicators
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 001-89661-7851
- Type
- D - Journal article
- DOI
-
10.1016/j.artmed.2013.12.002
- Title of journal
- Artificial Intelligence In Medicine
- Article number
- -
- First page
- 103
- Volume
- 60
- Issue
- 2
- ISSN
- 0933-3657
- Open access status
- Out of scope for open access requirements
- Month of publication
- February
- Year of publication
- 2014
- URL
-
https://www.sciencedirect.com/science/article/pii/S0933365713001620
- 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
-
4
- Research group(s)
-
2 - Software, Systems & Security (SSS)
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The work was part of a three-year EPSRC grant (Ref: EP/H019685/1) in collaboration with Moorfields Eye Hospital entitled: “Data Integrity and Intelligent Data Analysis Techniques Applied to a Glaucoma Progression Dataset”. The initial work with Moorfields led to significant further collaboration “post” grant with at least three Journal publications since and a number of IEEE Conference papers over successive years using the Moorfields data. The latest of these were published in 2019 (Jilani, Rucker and Swift (2019), Journal of Heuristics (https://doi.org/10.1007/s10732-019-09415-y).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
