1. SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data
- Author
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Öğretir, Mine, Koskinen, Miika, Sinisalo, Juha, Renkonen, Risto, and Lähdesmäki, Harri
- Subjects
Computer Science - Machine Learning - 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.
- Published
- 2024