ICU Mortality Prediction Using XGBoost-based Scoring Systems: A Study from a Developing Country
A locally adapted ICU mortality prediction model was developed, underscoring the pivotal role of predictors such as hospital stay duration, albumin, and urea levels in predicting patient outcomes. The heightened accuracy and sensitivity of the XGBoost model signify its potential as an invaluable tool in the critical task of mortality prediction within the Jordanian ICU context.
Authors
- Mohammad Araydah (ORCID: https://orcid.org/0000-0002-8440-8585)
- Reema Karasneh (ORCID: https://orcid.org/0000-0002-2919-1280)
- Suad Kabbaha (ORCID: https://orcid.org/0000-0003-3626-9215)
- Mamoon A. Aldeyab (ORCID: https://orcid.org/0000-0001-8148-7612)
- Dania Rahhal (ORCID: https://orcid.org/0000-0003-4718-1577)
- Karem H. Alzoubi (ORCID: https://orcid.org/0000-0002-2808-5099)
- Sayer I. Al-Azzam (ORCID: https://orcid.org/0000-0002-3414-7970)
- Zelal Jaber Kharaba (ORCID: https://orcid.org/0000-0001-8012-684X)
- Yamin Al-Azzam
Institutions
- University of Huddersfield (GB)
- Jordan University of Science and Technology (JO)
- University of Sharjah (AE)
- Impact (CA)
- Hamilton Health Sciences (CA)
- Yarmouk University (JO)
- McMaster University (CA)
Publication Details
- Journal
- Reviews on Recent Clinical Trials
- Published
- 2025-06-18
- DOI
- https://doi.org/10.2174/0115748871348585250604065542
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00