发表机构
Mayo Clinic（梅奥诊所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究探讨临床预后建模中生存结局二值化的代价，提出生存感知贝叶斯网络，用Cox偏对数似然替代二值评分函数，可恢复二值化遗漏的预后特征，其效果优于二值化方法。
AI 中文摘要
生存分析是分析事件发生时间数据的成熟框架，但许多临床机器学习研究仍会在模型训练前将结局二值化。这种做法会排除删失患者，将时间信息压缩为单一阈值，还会影响被选为预后相关的特征。我们以贝叶斯网络（BN）特征选择为背景，研究这种二值化的代价，以两篇近期发表的论文为案例：一篇将基于BN的特征选择应用于头颈部癌症队列，另一项虽非基于BN但同样对生存终点进行二值化的外科队列研究。我们将用于特征-结局边的二值评分函数替换为Cox偏对数似然，该改进我们称为生存感知贝叶斯网络，可恢复二值化遗漏的预后特征。我们的消融实验证实，改进源于事件发生时间评分公式，而非保留更多患者。结果在头颈部癌症的5个终点-队列组合中具有普适性，还可扩展至另外三种癌症类型（乳腺癌、结直肠癌、肾癌）。我们建议，含生存结局的临床研究应默认使用事件发生时间方法，因为二值化会丢弃生存分析保留的预后信号。
英文摘要
Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training. This practice excludes censored patients, collapses temporal information into a single threshold, and can affect which features are selected as prognostically relevant. We examine the cost of this binarization in the context of Bayesian network (BN) feature selection, using two recent publications as case studies: one that applies BN-based feature selection to a head-and-neck cancer cohort and a second surgical cohort study that, while not BN-based, likewise binarizes its survival endpoint. We replace the binary scoring function with the Cox partial log-likelihood for feature-to-outcome edges, a modification we call the Survival-Aware Bayesian network, and recover prognostic features that binarization misses. Our ablation experiment confirms that the improvement is driven by the time-to-event scoring formulation rather than by retaining more patients. The results generalize across five endpoint-cohort combinations in head-and-neck cancer and extend to three further cancer types (breast, colorectal, and kidney). We propose that clinical studies with survival outcomes should use time-to-event methods by default, as binarization discards the prognostic signal retained by survival analysis.
CommentsAccepted to Machine Learning for Healthcare (MLHC) Conference 2026
