Early hospital mortality prediction of intensive care unit patients using an ensemble learning approach
- Submitting institution
-
University of Portsmouth
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 7157638
- Type
- D - Journal article
- DOI
-
10.1016/j.ijmedinf.2017.10.002
- Title of journal
- International Journal of Medical Informatics
- Article number
- -
- First page
- 185
- Volume
- 108
- Issue
- -
- ISSN
- 1386-5056
- 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
- Yes
- Number of additional authors
-
3
- Research group(s)
-
A - Centre for Healthcare Modelling and Informatics
- Citation count
- 38
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- A result of collaboration between teams from the University of Portsmouth and Portsmouth Hospitals University NHS Trust. The study was conducted on one of the largest Intensive Care Unit (ICU) research datasets. The paper presented to the medical informatics community a novel machine learning method for early mortality prediction in intensive care units. The results show that the proposed method significantly outperforms current methods used in ICU. Its results were used by Einev et al (https://doi.org/10.1126/science.aar5045) to benchmark and validate their findings.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -