Quantifying Risk Factors in Medical Reports with a Context-Aware Linear Model
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 84759879
- Type
- D - Journal article
- DOI
-
10.1093/jamia/ocz004
- Title of journal
- Journal of the American Medical Informatics Association
- Article number
- -
- First page
- 537
- Volume
- 26
- Issue
- 6
- ISSN
- 1067-5027
- 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
-
2
- Research group(s)
-
A - Computer Science
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "This paper describes a new method for predicting mortality risk from electronic health records based on context aware linear modelling (CALM). Evaluation shows that the model performed as well as human experts.
Keynote at Underwrite conference, SIMBig 2019.
Enabled project Pacific Life Re GBP565,000.
Research is part of a product developed by Pacific Life, ''UnderwriteMe''.
Subsequent funding on the model, Lloyds Foundation with HSE, risk assessment analysis of accident incidents (GBP1,100,000).
Media: Featured in leading industry sector magazine, ""Cover""."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
