Updating Markov models to integrate cross-sectional and longitudinal studies
- Submitting institution
-
Brunel University London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 040-177109-5379
- Type
- D - Journal article
- DOI
-
10.1016/j.artmed.2017.03.005
- Title of journal
- Artificial Intelligence In Medicine
- Article number
- -
- First page
- 23
- Volume
- 77
- Issue
- -
- ISSN
- 0933-3657
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2017
- URL
-
http://bura.brunel.ac.uk/handle/2438/14688
- 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
-
2
- Research group(s)
-
1 - Artificial Intelligence (AI)
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The paper was selected to appear in a special issue based on a conference paper in the AI in Medicine conference (acceptance rate: 39%, acceptance rate of special issue: 10%). The work has been referred to in two Nature Communication reviews: Campbell, & Yau: https://doi.org/10.1038/s41467-018-04696-6 and Shen, L. et al. https://doi.org/10.1038/s41467-018-04633-7 A new PhD student in collaboration with Manchester and Pavia has extended this approach (https://doi.org/10.1016/j.artmed.2020.101930 ). The methods are tested and validated fully on both simulated data and numerous real clinical data including diabetes, high throughput sequencing and visual field data.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
