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Updated: Jan 12, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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可解释机器学习用于生存分析

Sophie Hanna Langbein1,2, Mateusz Krzyziński3, Mikołaj Spytek3

  • 1Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.

Biometrical journal. Biometrische Zeitschrift
|October 31, 2025
PubMed
概括

可解释机器学习 (IML) 对于医疗保健中的透明生存分析至关重要. 本研究回顾了IML方法,并展示了它们用于理解模型预测和识别风险因素的应用.

关键词:
在IML中,IML是IML.在XAI,XAI就是XAI.可以解释性的解释性.可解释的人工智能可以解释的机器学习.生存分析,生存分析.

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