English(EN) Cost-based variable selection for prediction models

新算法通过考虑变量成本来优化临床预测模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种新的临床预测模型变量选择算法，该算法考虑了测量每个变量相关的成本和危害。这种决策分析方法已在R包NBvarsel中实现，旨在通过确保变量预测能力的附加效益超过其测量带来的净效益降低，来优化临床效用。该方法在模拟和真实案例研究中进行了测试，证明了其能够生成精简模型，从而提高临床效用，同时最大限度地减少不必要的负担和成本。 AI

影响这项研究通过减少不必要的数据收集，有望带来更有效、更具成本效益的临床预测模型。

排序理由该集群包含一篇研究论文，详细介绍了预测模型中变量选择的新方法。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Lasai Barre\~nada, Andrew Vickers, Laure Wynants, Bieke Tack, Dirk Timmerman, Ewout Steyerberg, Ben Van Calster · 2026-10-28 04:00

用于预测模型的基于成本的变量选择

arXiv:2610.37353v1 Announce Type: cross Abstract: Objective: Variable selection for clinical prediction models is commonly done on statistical grounds related to model fit. Statistical approaches ignore the cost of misclassifications and the harm of measuring a variable. We aim t…

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用于预测模型的基于成本的变量选择

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