Use of machine learning algorithms to predict optimal hospital length of stay
Problem: Hospitals often struggle to allocate beds, equipment, and staff efficiently, leading to unnecessary complications. Predicting a patient’s length of stay (LOS) early helps hospitals plan treatment, staffing, and bed availability more effectively. Both extremes of LOS carry risks: discharging too early can result in inadequate care and higher readmissions, while prolonged stays waste resources and increase costs. Solution: Optimizing LOS improves patient outcomes using machine learning, enhances operational efficiency, and reduces overall spending.
Authors
- Almuatazbellah Boker (ORCID: https://orcid.org/0000-0002-9484-7266)
- Hoda Eldardiry (ORCID: https://orcid.org/0000-0002-9712-6667)
- Lara Khansa (ORCID: https://orcid.org/0000-0001-7305-5190)
- Okyaz Eminağa (ORCID: https://orcid.org/0000-0003-0861-3138)
- Habeeb Jimoh
- Adam Essawi
- Muhammet Isa Bagci
- Ola Abdelhad
Publication Details
- Journal
- VTechWorks (Virginia Tech)
- Published
- 2025-12-09
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00