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Updated: Feb 13, 2026

Ex Situ Normothermic Machine Perfusion of Donor Livers
12:13

Ex Situ Normothermic Machine Perfusion of Donor Livers

Published on: May 26, 2015

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機械学習によるセプシス関連の肝臓損傷の早期リスクの階層化:複数のコホートによる研究

Xin Chen1, Pinwen Zhou1, Jiaqi Wang1

  • 1Department of General Surgery, Nanjing Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.

Frontiers in medicine
|February 12, 2026
PubMed
まとめ

機械学習モデルは,早めにセプシス関連の肝損傷 (SRLI) を予測することができます. ランダムフォレストモデルは,重症患者のリスク分層化のためのプロトロンビン時間などの主要な予測要因を特定して,最高のパフォーマンスを示しました.

キーワード:
機械学習 (Machine Learning) とは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) と呼ばれるものです.予測モデルの予測モデルランダムフォレスト ランダムフォレストセプシス・セプシスとはセプシスに関連した肝損傷.

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