Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms
Our study suggests that ML algorithms, particularly LightGBM, show promise for predicting postoperative hemorrhage following brain tumor surgery. Biomarkers such as platelet count, chloride, and delta-CRP offer clinically meaningful insights for early risk detection. Once externally validated, the integration of such models into clinical decision support systems could potentially improve postoperative monitoring and patient outcomes.
Authors
- Hikmet Kocaman (ORCID: https://orcid.org/0000-0001-5971-7274)
- Hasan Yıldırım (ORCID: https://orcid.org/0000-0003-4582-9018)
- Şule Göktürk (ORCID: https://orcid.org/0000-0001-6590-4885)
- Yasin Göktürk (ORCID: https://orcid.org/0000-0002-4779-9927)
- Seyit Kağan Başarslan
Institutions
- University of Health Science (KH)
- Kayseri Eğitim ve Araştırma Hastanesi (TR)
- Sağlık Bilimleri Üniversitesi (TR)
- Karamanoğlu Mehmetbey University (TR)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1186/s12911-025-03245-8
- Citations
- 1
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 1.39