関連する実験動画

Updated: Sep 9, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

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時間依存生存モデルの予測性能を改善するための統計的学習方法

Hyungwoo Seo1, Wonil Chung2,3

  • 1Department of Statistics and Actuarial Science, Soongsil University, Seoul, 06978, South Korea.

Genomics & informatics
|September 2, 2025
PubMed
まとめ

生存モデルの時間間隔を精査することで,COVID-19のリスク評価が改善されます. 先進的なモデルと階層化された間隔は,進化する感染症の予測精度を高め,仮定が満たされると標準的な方法を上回ります.

キーワード:
コロナウイルスコックスの比例リスクモデルディープヒットディープサーヴランダムな生存森林時間依存生存モデル

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