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

• URL: http://arxiv.org/abs/2409.12709v1
• Date: Thu, 19 Sep 2024 12:35:25 GMT
• Title: SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data
• Authors: Mine Öğretir, Miika Koskinen, Juha Sinisalo, Risto Renkonen, Harri Lähdesmäki,
• Abstract summary: We propose SeqRisk, a method that combines variational autoencoder (VAE) or longitudinal VAE (LVAE) with a transformer encoder and Cox proportional hazards module for risk prediction. We demonstrate that SeqRisk performs competitively compared to existing approaches on both simulated and real-world datasets.
• Score: 4.1476925904032464
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In healthcare, risk assessment of different patient outcomes has for long time been based on survival analysis, 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 encoder and Cox proportional hazards module for risk prediction. SeqRisk captures long-range interactions, improves patient trajectory representations, enhances predictive accuracy and generalizability, as well as provides partial explainability for sample population characteristics in attempts to identify high-risk patients. We demonstrate that SeqRisk performs competitively compared to existing approaches on both simulated and real-world datasets.

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