Predictive Factors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Graft Surgery based on Machine Learning Methods.
The insights gained from the mashine learning model highlight the significance of demographic and clinical variables in predicting LOS in ICU. By understanding these predictors, healthcare professionals can better identify patients at higher risk for prolonged ICU stays.
Authors
- Behzad Imani (ORCID: https://orcid.org/0000-0002-1544-8196)
- Soheila Saeedi (ORCID: https://orcid.org/0000-0003-1315-794X)
- Amir Shams (ORCID: https://orcid.org/0000-0003-4460-989X)
- Alireza Jafarkhani (ORCID: https://orcid.org/0000-0002-1566-5203)
Institutions
- Hamedan University of Medical Sciences (IR)
Publication Details
- Journal
- PubMed
- Published
- 2025-01-01
- DOI
- https://doi.org/10.22037/aaemj.v13i1.2595
- Citations
- 1
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.35