Predicting whether patients in an acute medical unit are physiologically fit-for-discharge using machine learning: A proof-of-concept
This proof-of-concept study showed that it is feasible to predict with machine learning whether patients in an Acute Medical Unit are physiologically fit-for-discharge using commonly available hospital data.
Authors
- Frank H. Bosch (ORCID: https://orcid.org/0000-0003-1172-6352)
- Carlijn A. Vernooij (ORCID: https://orcid.org/0000-0001-6612-037X)
- Mark Vincent Koning (ORCID: https://orcid.org/0000-0002-5465-3504)
- Niels Kant (ORCID: https://orcid.org/0009-0007-6777-7307)
- Carine J.M. Doggen
- Wim Verhaegh (ORCID: https://orcid.org/0000-0002-8088-9924)
- Sandra F. Oude Wesselink
- Bernard P. Veldkamp (ORCID: https://orcid.org/0000-0003-3543-2164)
- Sjoerd H. Garssen (ORCID: https://orcid.org/0000-0001-9994-5511)
Institutions
- NXP (Netherlands) (NL)
- Radboud University Nijmegen (NL)
- Radboud University Medical Center (NL)
- Philips (Netherlands) (NL)
- Rijnstate Hospital (NL)
- University of Twente (NL)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2024-08-10
- DOI
- https://doi.org/10.1016/j.ijmedinf.2024.105586
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.35