Clinically Applicable Machine Learning Approach to Predict Intracerebral Hematoma Expansion
A predictive model for HE was developed using the medical history, clinical features available on the patient's arrival, imaging, and radiomics features extracted from computed tomography images. This prediction model will assist non-stroke care specialists in making treatment decisions for ICH in emergency settings.
Authors
- Shogo Watanabe
- Nobutaka Mukae (ORCID: https://orcid.org/0000-0003-1990-1485)
- Koji Iihara (ORCID: https://orcid.org/0000-0002-7852-220X)
- Nice Ren (ORCID: https://orcid.org/0000-0003-4702-2760)
- Yukihiro Imaoka (ORCID: https://orcid.org/0000-0002-7054-2708)
- K. Arimura
- Kunihiro Nishimura
- Syoji Kobashi
- Kento Morita
- J‐ASPECT study collaborators
Institutions
- Kyushu University (JP)
- Mie University (JP)
- University of Hyogo (JP)
- National Cerebral and Cardiovascular Center (JP)
Publication Details
- Journal
- Journal of the American Heart Association
- Published
- 2025-12-30
- DOI
- https://doi.org/10.1161/jaha.125.042387
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00