Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support
- Submitting institution
-
Oxford Brookes University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 185741464
- Type
- D - Journal article
- DOI
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10.1016/j.artmed.2017.09.007
- Title of journal
- Artificial Intelligence in Medicine
- Article number
- -
- First page
- 28
- Volume
- 85
- Issue
- -
- ISSN
- 0933-3657
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- 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
-
4
- Research group(s)
-
-
- Citation count
- 10
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The novel aspect of the our approach is the use of temporal sequences to include preceding events in the decision making process. This improves the accuracy of the predictions and reduces the blood glucose risk index (which shows the probability of hypoglycaemia or hyperglycaemia) by nearly one third, according to simulation results. Research on temporal representations for CBR is rare, the majority of CBR research being instant-based, even though many situations extend over a time-bounded window. The student working on this project secured a post as a senior developer at TripAdvisor following his work on this project.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -