Additional file 7 of Machine learning-based prediction of short-term outcomes in aneurysmal subarachnoid hemorrhage: a multicenter study integrating clinical and inflammatory indicators

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Authors: Weichong Zhou, Cong Peng, Peize Li, Yufei Li, Xingfu Liao, Zhuo Wang, Mingfeng Wang, Yue Xiao, Hai Su, Hui Shi
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Abstract

Additional file 7. Fig S6: LIME interpretation of the GBM model. (A) Ranked feature contributions: bar length indicates contribution magnitude, and colors denote direction of effect. The top nine predictors were CLR, WFNS, PNI, GCS, PLR, modified Fisher grade, procalcitonin, NAR, and SII. (B) Example case prediction: probability of favorable outcome 0.89 and unfavorable outcome 0.11. WFNS ≤ 1 and higher GCS contributed positively, whereas elevated CLR had a negative impact.Note:PNI 1 (48.8), NAR 1 (0.31), PLR 1 (295),SII 1 (3037.3),SIRI 1 (9.68), Procalcitonin 1 (

Publication details

Journal / source
Figshare
Publication date
1 January 2026
Document type
Dataset