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.

翻译：在医疗健康领域，患者结局的风险评估长期以来一直基于生存分析，即对时间-事件关联进行建模。然而，传统方法依赖于单一时间点的数据，这使其无法充分利用纵向患者病史并捕捉时间规律。本研究聚焦于临床真实世界数据并正视其挑战，利用潜变量模型有效处理不规则、噪声大且稀疏观测的纵向数据。我们提出了SeqRisk方法，该方法将变分自编码器（VAE）或纵向VAE（LVAE）与基于Transformer的序列聚合模块以及Cox比例风险模块相结合，用于风险预测。SeqRisk能够捕获长程交互作用，提高预测准确性和泛化能力，并为样本群体特征提供部分可解释性，以尝试识别高风险患者。在数据稀疏性增加的条件下，SeqRisk展现出鲁棒的性能，持续超越现有方法。