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arXiv · 2409.12709

SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data

Mine Öğretir·Miika Koskinen·Juha Sinisalo·Risto Renkonen·Harri Lähdesmäki
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Abstract

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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Mine Öğretir, Miika Koskinen, Juha Sinisalo, Risto Renkonen, Harri Lähdesmäki. 2024-09-19. SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data. https://arxiv.org/abs/2409.12709

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