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Updated: Feb 23, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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急性E型肝炎における死亡率予測のための代謝因子ベースの機械学習モデル: デュアルセンターコホートから開発および検証

Haoshuang Fu1, Shuying Song1, Yuelin Xiao1

  • 1Department of Infectious Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
|February 21, 2026
PubMed
まとめ

新しいロジスティック回帰モデルは,代謝因子を用いて,E型肝炎 (HEV) 患者の短期的な死亡率を正確に予測します. このモデルは,早期のリスク分層化とHEV感染のパーソナライズされた管理に役立ちます.

キーワード:
E型肝炎ウイルスは,機械学習 (Machine Learning) とは,機械学習 (Machine Learning) というものです.非侵襲的ではない.予測モデルの予測モデル

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In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess...
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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
A recent model describes pravastatin's hepatobiliary excretion,...
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