Temporal Comorbidity-Adjusted Risk of Emergency Readmission (T-CARER): A Tool for Comorbidity Risk Assessment
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- qq564
- Type
- D - Journal article
- DOI
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10.1016/j.asoc.2019.03.015
- Title of journal
- Applied Soft Computing
- Article number
- -
- First page
- 163
- Volume
- 79
- Issue
- -
- ISSN
- 1568-4946
- 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)
-
-
- Citation count
- 2
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Quantification of high-risk diagnoses, operations and procedures, and monitoring changes over time can significantly improve the quality of readmission models. However, adjusting for demographic and temporal patterns, and advanced machine learning methods have not been explored in previous research. The developed solutions are trained, tested and cross-validated across several samples from a 10-year of England hospital inpatient database, and benchmarked against popular comorbidity models, including Charlson and Elixhauser Comorbidity indices. The produced solution and software toolkit have been publicly shared and have sufficient generality to be extended to other healthcare settings.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
