Stepwise Fine and Gray: Subject-Specific Variable Selection Shows When Hemodynamic Data Improves Prognostication of Comatose Post-Cardiac Arrest Patients

Xiaobin Shen; Jonathan Elmer; George H. Chen

2025 MLHC MLHC 2025

Stepwise Fine and Gray: Subject-Specific Variable Selection Shows When Hemodynamic Data Improves Prognostication of Comatose Post-Cardiac Arrest Patients

Abstract

No abstract available. No abstract was captured for this paper when its proceedings were scraped - most often an older edition published without one online. How the corpus is built.

Authors

Xiaobin Shen, Jonathan Elmer, George H. Chen

Topics

Artificial Intelligence > Core AI > Interpretability Machine Learning > Core Methods > Classification Deep Learning > Architectures > Neural Networks

Keywords

dynamic competing risks fine and gray model four score hemodynamic data neurological outcomes prognostication

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