Development and validation of a machine learning model for early prediction of intensive care unit acquired weakness
Machine learning models, especially random forests, can be used to predict if patients are at risk of developing ICU-AW, using data available within 24 h of admission. This tool allows prognostication early in an adult general critically ill patient population, with the potential to detect high cost and high need patients who benefit from different levels of care.
Authors
- Grégoire Coppens (ORCID: https://orcid.org/0000-0001-7637-3549)
- Ilse Vanhorebeek (ORCID: https://orcid.org/0000-0002-5261-5192)
- Celine Vens (ORCID: https://orcid.org/0000-0003-0983-256X)
- Felipe Kenji Nakano (ORCID: https://orcid.org/0000-0002-4884-9420)
- Nathalie Van Aerde (ORCID: https://orcid.org/0000-0002-1331-3052)
- Fabián Güiza (ORCID: https://orcid.org/0000-0001-7026-0957)
- Greet Van den Berghe (ORCID: https://orcid.org/0000-0002-5320-1362)
Institutions
- Imec the Netherlands (NL)
- IMEC (BE)
- Department of Public Health (MM)
- KU Leuven (BE)
Publication Details
- Journal
- Intensive Care Medicine Experimental
- Published
- 2025-09-30
- DOI
- https://doi.org/10.1186/s40635-025-00810-3
- Citations
- 1
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- Vlaamse regering