Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.17649 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918318386970624 |
|---|---|
| author | Ding, Yilang Ren, Jiawen Lu, Jiaying Kwak, Gloria Hyunjung Iraji, Armin Tang, Shengpu Fedorov, Alex |
| author_facet | Ding, Yilang Ren, Jiawen Lu, Jiaying Kwak, Gloria Hyunjung Iraji, Armin Tang, Shengpu Fedorov, Alex |
| contents | Alzheimer's disease is a progressive neurodegenerative disorder that remains challenging to predict due to its multifactorial etiology and the complexity of multimodal clinical data. Accurate forecasting of clinically relevant biomarkers, including diagnostic and quantitative measures, is essential for effective monitoring of disease progression. This work introduces L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional (L2C) transformation with a pre-trained Tabular Foundation Model (TabPFN) to predict Alzheimer's disease outcomes using the TADPOLE dataset. L2C-TabPFN converts sequential patient records into fixed-length feature vectors, enabling robust prediction of diagnosis, cognitive scores, and ventricular volume. Experimental results demonstrate that, while L2C-TabPFN achieves competitive performance on diagnostic and cognitive outcomes, it provides state-of-the-art results in ventricular volume prediction. This key imaging biomarker reflects neurodegeneration and progression in Alzheimer's disease. These findings highlight the potential of tabular foundational models for advancing longitudinal prediction of clinically relevant imaging markers in Alzheimer's disease. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_17649 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model Ding, Yilang Ren, Jiawen Lu, Jiaying Kwak, Gloria Hyunjung Iraji, Armin Tang, Shengpu Fedorov, Alex Machine Learning Alzheimer's disease is a progressive neurodegenerative disorder that remains challenging to predict due to its multifactorial etiology and the complexity of multimodal clinical data. Accurate forecasting of clinically relevant biomarkers, including diagnostic and quantitative measures, is essential for effective monitoring of disease progression. This work introduces L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional (L2C) transformation with a pre-trained Tabular Foundation Model (TabPFN) to predict Alzheimer's disease outcomes using the TADPOLE dataset. L2C-TabPFN converts sequential patient records into fixed-length feature vectors, enabling robust prediction of diagnosis, cognitive scores, and ventricular volume. Experimental results demonstrate that, while L2C-TabPFN achieves competitive performance on diagnostic and cognitive outcomes, it provides state-of-the-art results in ventricular volume prediction. This key imaging biomarker reflects neurodegeneration and progression in Alzheimer's disease. These findings highlight the potential of tabular foundational models for advancing longitudinal prediction of clinically relevant imaging markers in Alzheimer's disease. |
| title | Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.17649 |