Application of machine learning for delirium prediction and analysis of associated factors in hospitalized COVID-19 patients: A comparative study using the Korean Multidisciplinary cohort for delirium prevention (KoMCoDe)
This research developed and validated an RF-based ML model for predicting delirium in hospitalized COVID-19 patients. The model demonstrates superior accuracy and reliability compared to other ML methods and would possibly serve as a valuable tool for managing and anticipating delirium in COVID-19 patients, with the potential to enhance patient outcomes.
Authors
- Sang Jun Han
- Eileen Laureal Yoon (ORCID: https://orcid.org/0000-0003-0474-048X)
- Arum Hong
- Yuna Jang (ORCID: https://orcid.org/0009-0004-2554-6728)
- Myeongju Kim (ORCID: https://orcid.org/0000-0003-0543-3019)
- Hye Yoon Park (ORCID: https://orcid.org/0000-0003-4114-5102)
- H.-Y. Sohn
Institutions
- Seoul National University Hospital (KR)
- Seoul National University Bundang Hospital (KR)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1016/j.ijmedinf.2024.105747
- Citations
- 2
- Primary Topic
- Intensive Care Unit Cognitive Disorders
- Type
- article
- Field-Weighted Citation Impact
- 0.84
Funders
- Ministry of Health and Welfare
- Korea Health Industry Development Institute