Machine learning prediction of unexpected readmission or death after discharge from intensive care: A retrospective cohort study
ML models were able to identify patients that will not need unplanned ICU readmission and will not die within 48 h after discharge.
Authors
- Jens Meier (ORCID: https://orcid.org/0000-0002-8727-2257)
- Thomas Tschoellitsch (ORCID: https://orcid.org/0000-0002-3350-7532)
- Michael Giretzlehner (ORCID: https://orcid.org/0000-0003-1620-1002)
- Philipp Moser (ORCID: https://orcid.org/0000-0002-9717-6197)
- Stefan Thumfart (ORCID: https://orcid.org/0000-0001-9838-3641)
- Sepp Hochreiter (ORCID: https://orcid.org/0000-0001-7449-2528)
- Alexander Maletzky (ORCID: https://orcid.org/0000-0003-4378-7854)
- Philipp Seidl (ORCID: https://orcid.org/0000-0003-4333-2040)
- Carl Böck (ORCID: https://orcid.org/0000-0003-2207-2411)
- Tina Tomić Mahečić (ORCID: https://orcid.org/0000-0003-2470-6238)
Institutions
- Johannes Kepler University of Linz (AT)
- University Hospital Centre Zagreb (HR)
- Kepler Universitätsklinikum (AT)
- RISC Software (Austria) (AT)
Publication Details
- Journal
- Journal of Clinical Anesthesia
- Published
- 2024-10-14
- DOI
- https://doi.org/10.1016/j.jclinane.2024.111654
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 1.39