Machine Learning–Based Prediction for In‐Hospital Mortality After Acute Intracerebral Hemorrhage Using Real‐World Clinical and Image Data
ML-based prediction models exhibit satisfactory performance in predicting post-ICH in-hospital mortality when utilizing raw imaging data or nonspecialist input. Nevertheless, incorporating specialist expertise notably improves performance.
Authors
- Hidehisa Soejima (ORCID: https://orcid.org/0000-0001-8550-7481)
- Naoki Nakashima (ORCID: https://orcid.org/0000-0003-4926-8772)
- Masahiro Kamouchi (ORCID: https://orcid.org/0000-0002-5348-8762)
- Shigeo Yamashiro (ORCID: https://orcid.org/0000-0003-4634-803X)
- Koutarou Matsumoto (ORCID: https://orcid.org/0000-0003-0769-7763)
- Kazuaki Ishihara (ORCID: https://orcid.org/0000-0003-4191-3845)
- Katsuhiko Matsuda (ORCID: https://orcid.org/0009-0008-4861-8076)
- Koki Tokunaga (ORCID: https://orcid.org/0009-0005-7525-8094)
Institutions
- Kurume University (JP)
- Kyushu University (JP)
- Saiseikai Kumamoto Hospital (JP)
- Kyushu University Hospital (JP)
Publication Details
- Journal
- Journal of the American Heart Association
- Published
- 2024-12-10
- DOI
- https://doi.org/10.1161/jaha.124.036447
- Citations
- 11
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 4.06
Funders
- Fujifilm Corporation