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Updated: Jun 7, 2025

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权衡准确性和可解释性:一个R包评估复杂的关系超出了考克斯模型和应用到临床预测

Diana Shamsutdinova1, Daniel Stamate2, Daniel Stahl1

  • 1Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.

International journal of medical informatics
|November 15, 2024
PubMed
概括

在复杂的临床数据中,机器学习 (ML) 生存模型在复杂的临床数据中表现优于传统的Cox比例危险模型 (Cox-PH),但在表格数据中并不总是如此. 一个新的R包,survcompare,通过评估准确性和可解释性来帮助选择最佳的临床预测模型.

关键词:
临床预测模型的临床预测模型.集合方法 集合方法内部验证内部验证可以解释性 解释性在这个过程中,R是R.对生存分析的分析.

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