関連する実験動画

Updated: Feb 15, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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AKI患者のセプシス性ICU患者におけるセプシス関連脳病変を予測するための解釈可能な4要素1日ノモグラム:MIMIC-IVにおける開発と内部検証

Zhiyang Zhang1, Ze Zhang1, Dandan Li1

  • 1Department of Intensive Care Unit, Hebei General Hospital, Shijiazhuang City, China.

Medicine
|February 13, 2026
PubMed
まとめ

ICU患者のセプシス関連脳性病 (SAE) リスクを,通常の1日目のデータを用いて予測するための,シンプルで透明な4要素モデルを開発しました. このツールは,早期のリスク分層化を助け,より良い患者のアウトカムのための潜在的な介入を特定します.

キーワード:
MIMIC-IV データベース集中治療室の重症病棟で集中治療を受けています.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.リスク予測モデルのモデルです.セプシスに関連した脳症候群.

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