Enhancing mortality prediction in patients with spontaneous intracerebral hemorrhage: Radiomics and supervised machine learning on non-contrast computed tomography
Radiomics-based Supervised Machine Learning models can predict mortality during admission in patients with sICH. SVM with the "DropOut+SelectKBest" feature selection strategy and no hyperparameter optimization was the best simplified model to detect mortality during admission in patients with sICH.
Authors
- Antonio López‐Rueda (ORCID: https://orcid.org/0000-0001-7914-9948)
- Alejandro Rodríguez (ORCID: https://orcid.org/0000-0001-8828-5984)
- Javier Moreno (ORCID: https://orcid.org/0000-0002-0750-2797)
- Laura Llull
- Elena Serrano (ORCID: https://orcid.org/0009-0005-9885-5537)
- Laura Oleaga (ORCID: https://orcid.org/0000-0001-9702-0451)
- Sergi Amaro
- María-Ángeles Rodríguez-Sánchez
Institutions
- Bellvitge University Hospital (ES)
- Hospital Clínic de Barcelona (ES)
- Artificial Intelligence Research Institute (ES)
- Universitat Politècnica de València (ES)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1016/j.ejro.2024.100618
- Citations
- 2
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 0.74