Genetics-Driven Personalized Disease Progression Model
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917941061091328 |
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| author | Yang, Haoyu Dey, Sanjoy Meyer, Pablo |
| author_facet | Yang, Haoyu Dey, Sanjoy Meyer, Pablo |
| contents | Modeling disease progression through multiple stages is critical for clinical decision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney diseases, and so on. Existing approaches often model the disease progression as a uniform trajectory pattern at the population level. However, chronic diseases are highly heterogeneous and often have multiple progression patterns depending on a patient's individual genetics and environmental effects due to lifestyles. We propose a personalized disease progression model to jointly learn the heterogeneous progression patterns and groups of genetic profiles. In particular, an end-to-end pipeline is designed to simultaneously infer the characteristics of patients from genetic markers using a variational autoencoder and how it drives the disease progressions using an RNN-based state-space model based on clinical observations. Our proposed model shows improvement on real-world and synthetic clinical data. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_00028 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genetics-Driven Personalized Disease Progression Model Yang, Haoyu Dey, Sanjoy Meyer, Pablo Machine Learning Artificial Intelligence Modeling disease progression through multiple stages is critical for clinical decision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney diseases, and so on. Existing approaches often model the disease progression as a uniform trajectory pattern at the population level. However, chronic diseases are highly heterogeneous and often have multiple progression patterns depending on a patient's individual genetics and environmental effects due to lifestyles. We propose a personalized disease progression model to jointly learn the heterogeneous progression patterns and groups of genetic profiles. In particular, an end-to-end pipeline is designed to simultaneously infer the characteristics of patients from genetic markers using a variational autoencoder and how it drives the disease progressions using an RNN-based state-space model based on clinical observations. Our proposed model shows improvement on real-world and synthetic clinical data. |
| title | Genetics-Driven Personalized Disease Progression Model |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2503.00028 |