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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.22018 |
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| _version_ | 1866915816636678144 |
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| author | de Jonge, Sterre Vinke, Elisabeth J. Vernooij, Meike W. Alexander, Daniel C. Young, Alexandra L. Bron, Esther E. |
| author_facet | de Jonge, Sterre Vinke, Elisabeth J. Vernooij, Meike W. Alexander, Daniel C. Young, Alexandra L. Bron, Esther E. |
| contents | Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22018 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data de Jonge, Sterre Vinke, Elisabeth J. Vernooij, Meike W. Alexander, Daniel C. Young, Alexandra L. Bron, Esther E. Machine Learning Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable tool to gain a deeper understanding of diseases with a long disease trajectory, such as Alzheimer's disease. A key limitation of most disease progression models is that they are specific to a single data type (e.g., continuous data), thereby limiting their applicability to heterogeneous, real-world datasets. To address this limitation, we propose the Mixed Events model, a novel disease progression model that handles both discrete and continuous data types. This model is implemented within the Subtype and Stage Inference (SuStaIn) framework, resulting in Mixed-SuStaIn, enabling subtype and progression modeling. We demonstrate the effectiveness of Mixed-SuStaIn through simulation experiments and real-world data from the Alzheimer's Disease Neuroimaging Initiative, showing that it performs well on mixed datasets. The code is available at: https://github.com/ucl-pond/pySuStaIn. |
| title | Disease Progression and Subtype Modeling for Combined Discrete and Continuous Input Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.22018 |