SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data
<|reference_start|>SeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data: 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.<|reference_end|>
@article{öğretir2024seqrisk:,
    title={SeqRisk: Transformer-augmented latent variable model for improved
  survival prediction with longitudinal data},
    author={Mine "Ou{g}retir, Miika Koskinen, Juha Sinisalo, Risto Renkonen and
  Harri L"ahdesm"aki},
    journal={arXiv preprint arXiv:2409.12709},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.12709},
    primaryClass={cs.LG}
}