Prediction of Intensive Care Length of Stay for Surviving and Nonsurviving Patients Using Deep Learning
This novel LOS model represents a step forward in achieving more equitable benchmarking across diverse ICU settings with varying risk profiles.
Authors
- Omar Badawi (ORCID: https://orcid.org/0000-0001-8518-9006)
- Ludmila Brochini (ORCID: https://orcid.org/0000-0002-4017-0157)
- Xinggang Liu
- Robin French
- Pamela J. Amelung (ORCID: https://orcid.org/0000-0002-4023-128X)
- Louis Atallah (ORCID: https://orcid.org/0000-0002-6657-319X)
Institutions
- NXP (Netherlands) (NL)
- Philips (Finland) (FI)
- Johnson & Johnson (United States) (US)
- Telemedicine & Advanced Technology Research Center (US)
- Philips (United States) (US)
Publication Details
- Journal
- Critical Care Medicine
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1097/ccm.0000000000006588
- Citations
- 2
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.71