A Comparative Study of Shallow and Deep Learning Models for Predicting Post-Operative Complications in Neurosurgical and Clinical Applications with Real-world Example (P1-2.007)
exploring the potential uses of LLM and SML models in predicting post-operative complications in patients with cervical spondylosis, and to compare the pros and cons of the two approaches in terms of accuracy, cost-effectiveness, and patient confidentiality and data security.
Authors
- Mostafa M. Eltobgy (ORCID: https://orcid.org/0000-0002-4056-7429)
- Ahmed Negida (ORCID: https://orcid.org/0000-0001-5363-6369)
- Ahmed Shaheen (ORCID: https://orcid.org/0000-0002-2216-1951)
- Ram Saha (ORCID: https://orcid.org/0000-0002-3398-9973)
- Nour Shaheen (ORCID: https://orcid.org/0000-0001-8663-4044)
- Basant Hamed (ORCID: https://orcid.org/0000-0002-1555-3818)
Institutions
- Virginia Commonwealth University (US)
- The Ohio State University Wexner Medical Center (US)
- Alexandria University (EG)
Publication Details
- Journal
- Neurology
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1212/wnl.0000000000211130
- Primary Topic
- Medical Imaging and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00