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Updated: Feb 13, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

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トラウマ性脳損傷における解圧性頭切除術後の血腫の拡大または再出血を予測するための臨床的および放射線学的特徴に基づく説明可能な機械学習モデル:バイセントリックコホート研究

Yingying Ding1, Liang Yao1, Yue Wang2

  • 1Department of Neurosurgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, Jiangsu Province, China.

International journal of surgery (London, England)
|February 12, 2026
PubMed
まとめ

トラウマ性脳損傷患者の解圧頭蓋切除術後の血腫の拡大を予測するために,解釈可能な機械学習モデルを開発しました. このツールは,早期のリスク評価と臨床意思決定を助け,患者のアウトカムを改善します.

キーワード:
SHAPの解釈可能性XGBoostは,XGBoostと一致している.デコンプレッシブクランエクトミー再び出血する.トラウマ性脳損傷による脳外傷.

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