Assessing performance, calibration, and explainability of machine learning versus traditional models for early outcome prediction after spontaneous intracerebral hemorrhage: a systematic review and meta-analysis protocol.
- DOI
- 10.1186/s13643-025-03059-9
- Published
- 2026 Jan 10
- Container
- Systematic reviews
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1186/s13643-025-03059-9,
title = {Assessing performance, calibration, and explainability of machine learning versus traditional models for early outcome prediction after spontaneous intracerebral hemorrhage: a systematic review and meta-analysis protocol.},
author = {Bu F and Xu R and Zhao X and He Q and Wen Y and Xiong L and Qin L and Guan H},
year = {2026},
journal = {Systematic reviews},
doi = {10.1186/s13643-025-03059-9},
url = {https://doi.org/10.1186/s13643-025-03059-9}
}RIS
TY - JOUR TI - Assessing performance, calibration, and explainability of machine learning versus traditional models for early outcome prediction after spontaneous intracerebral hemorrhage: a systematic review and meta-analysis protocol. AU - Bu F AU - Xu R AU - Zhao X AU - He Q AU - Wen Y AU - Xiong L AU - Qin L AU - Guan H PY - 2026 JO - Systematic reviews DO - 10.1186/s13643-025-03059-9 UR - https://doi.org/10.1186/s13643-025-03059-9 ER -
APA
F, B., R, X., X, Z., Q, H., Y, W., L, X., L, Q., & H, G. (2026). Assessing performance, calibration, and explainability of machine learning versus traditional models for early outcome prediction after spontaneous intracerebral hemorrhage: a systematic review and meta-analysis protocol.. Systematic reviews. https://doi.org/10.1186/s13643-025-03059-9
Source records
- pubmed · retrieved 2026-09-27T07:47:55.080Z