SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915804779380736 |
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| author | Öğretir, Mine Koskinen, Miika Sinisalo, Juha Renkonen, Risto Lähdesmäki, Harri |
| author_facet | Öğretir, Mine Koskinen, Miika Sinisalo, Juha Renkonen, Risto Lähdesmäki, Harri |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12709 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data Öğretir, Mine Koskinen, Miika Sinisalo, Juha Renkonen, Risto Lähdesmäki, Harri Machine Learning 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. |
| title | SeqRisk: Transformer-augmented latent variable model for robust survival prediction with longitudinal data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.12709 |