Predicting hypoglycemia in ICU patients: a machine learning approach
We used machine learning to anticipate the likelihood of hypoglycemia, which can significantly decrease hypoglycemia incidents and enhance patient outcomes.
Authors
- 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)
- Aisha F. Badr (ORCID: https://orcid.org/0000-0002-5067-2441)
- Sayer I. Al-Azzam (ORCID: https://orcid.org/0000-0002-3414-7970)
- Muna A. L. Ebbini
- Asma’a Alselwi
Institutions
- University of Huddersfield (GB)
- Jordan University of Science and Technology (JO)
- King Abdulaziz University (SA)
- University of Sharjah (AE)
- Impact (CA)
- Yarmouk University (JO)
- McMaster University (CA)
Publication Details
- Journal
- Expert Review of Endocrinology & Metabolism
- Published
- 2024-09-02
- DOI
- https://doi.org/10.1080/17446651.2024.2403039
- Primary Topic
- Diabetes Management and Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00