用Transformer增强隐变量模型，提升纵向医疗数据的生存预测能力
SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data
- 结合VAE与Transformer，建模患者长期医疗记录的时序特征
- 在数据稀疏条件下仍优于现有方法，显著提升预测准确率
- 适合关注医疗风险预测与可解释性的研究者使用
在医疗领域，患者预后风险评估长期依赖生存分析，即建模事件发生时间关系。然而，传统方法仅使用单次时间点数据，难以充分利用患者的纵向病史并捕捉时间规律。针对真实世界临床数据的不规则、噪声大和观测稀疏等挑战，我们采用隐变量模型有效处理此类数据。提出SeqRisk方法，将变分自编码器（VAE）或纵向变分自编码器（LVAE）与基于Transformer的序列聚合模块及Cox比例风险模型相结合，用于风险预测。该方法能捕捉长程依赖关系，提升预测精度与泛化能力，并提供部分可解释性，帮助识别高风险人群。在数据稀疏程度增加的情况下，SeqRisk表现稳健，持续优于现有方法。
原文摘要 · Abstract (English)
In healthcare, risk assessment of patient outcomes has been based on survival analysis for a long time, i.e. modeling time-to-event associations. However, conventional approaches rely on data from a single time-point, making them suboptimal for fully leveraging longitudinal patient history and capturing temporal regularities. Focusing on clinical real-world data and acknowledging its challenges, we utilize latent variable models to effectively handle irregular, noisy, and sparsely observed longitudinal data. We propose SeqRisk, a method that combines variational autoencoder (VAE) or longitudinal VAE (LVAE) with a transformer-based sequence aggregation and Cox proportional hazards module for risk prediction. SeqRisk captures long-range interactions, enhances predictive accuracy and generalizability, as well as provides partial explainability for sample population characteristics in attempts to identify high-risk patients. SeqRisk demonstrated robust performance under conditions of increasing sparsity, consistently surpassing existing approaches.
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