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Updated: Feb 16, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
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機械学習ベースの予後モデルで,通常の指標を用いて,セプシスに関連した肝臓損傷を予測する.

Wenjun Zhu, Jinmi Li, Yiming Yang

    Medical principles and practice : international journal of the Kuwait University, Health Science Centre
    |February 14, 2026
    PubMed
    まとめ

    機械学習モデルは,通常のバイオマーカーを使用して,セプシス関連肝損傷 (SALI) の予後を予測します. ランダムフォレストモデルは,臨床的決定を導き,SALI患者の治療結果を改善する見通しを示しています.

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