用掩码建模生成临床生存数据，提升模型预测效果。
Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation
- 借鉴语言模型掩码思想，实现数据合成与条件增强。
- 生成数据使生存分析的区分度和校准度优于现有方法。
- 适合需要隐私保护的医疗研究者使用。
真实临床数据因隐私限制难以获取，制约了医疗研究。合成数据可提供安全共享与模型开发方案，但多数方法关注数据真实性而非实用性——即合成数据训练的模型能否产生与真实数据相当的临床洞察。本文提出掩码临床建模（Masked Clinical Modelling, MCM），受掩码语言建模启发，用于数据合成与条件增强。我们在WHAS500数据集上以Cox比例风险模型评估，重点考察危险比（hazard ratios）的保留情况。结果表明，使用MCM生成的数据在生存分析中显著提升区分度与校准度，优于现有方法。MCM展现出支持生存分析及更广泛医疗应用的潜力。
原文摘要 · Abstract (English)
Access to real clinical data is often restricted due to privacy obligations, creating significant barriers for healthcare research. Synthetic datasets provide a promising solution, enabling secure data sharing and model development. However, most existing approaches focus on data realism rather than utility -- ensuring that models trained on synthetic data yield clinically meaningful insights comparable to those trained on real data. In this paper, we present Masked Clinical Modelling (MCM), a framework inspired by masked language modelling, designed for both data synthesis and conditional data augmentation. We evaluate this prototype on the WHAS500 dataset using Cox Proportional Hazards models, focusing on the preservation of hazard ratios as key clinical metrics. Our results show that data generated using the MCM framework improves both discrimination and calibration in survival analysis, outperforming existing methods. MCM demonstrates strong potential to support survival data analysis and broader healthcare applications.
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