Evaluating the Machine Learning Models in Predicting Intensive Care Unit Discharge for Neurosurgical Patients Undergoing Craniotomy: A Big Data Analysis
Random forest and NN predict ICU discharge well, whereas LR is interpretable but less accurate. Numeric conversion of clinical data improved performance. This study offers framework for predictions using clinical, radiological, and demographic features, with SHAP enhancing transparency.
Authors
- Taghi Khaniyev (ORCID: https://orcid.org/0000-0002-2160-0553)
- M. Koc
- Şahin Hanalıoğlu (ORCID: https://orcid.org/0000-0003-4988-4938)
- Efecan Çekiç (ORCID: https://orcid.org/0000-0003-2203-5944)
- Ilke Dogan
Institutions
- Brigham and Women's Hospital (US)
- Bilkent University (TR)
- Massachusetts Institute of Technology (US)
- Hacettepe University (TR)
Publication Details
- Journal
- Neurocritical Care
- Published
- 2025-05-06
- DOI
- https://doi.org/10.1007/s12028-025-02246-9
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48
Funders
- Türkiye Bilimsel ve Teknolojik Araştırma Kurumu
- Hacettepe Üniversitesi