Dynamic classification using credible intervals in longitudinal discriminant analysis
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1055
- Type
- D - Journal article
- DOI
-
10.1002/sim.7397
- Title of journal
- Statistics in Medicine
- Article number
- -
- First page
- 3858
- Volume
- 36
- Issue
- 24
- ISSN
- 0277-6715
- Open access status
- Compliant
- Month of publication
- August
- 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
- Yes
- Number of additional authors
-
4
- Research group(s)
-
-
- Citation count
- 5
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Current AI-based medical decision-support systems estimate the probability of effective treatment, but not associated uncertainty. The work developed new mathematical methods in the framework of generalised multivariate mixed effect models and Bayesian estimates of the credible intervals for the probability of treatment effectiveness. Supported by MRC Methodology Research Panel funding (£334K, DiALog, MR/L010909/1), this has led to a reliable, early predictor of seizure reoccurrence in epilepsy patients for more effective patient management and counselling (Neurology, 91:E2035-E2044 DOI: https://doi.org/10.1212/WNL.0000000000006564 ).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
