Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model
This work addresses the challenge of forecasting clinically relevant biomarkers for Alzheimer's disease, offering incremental improvements in prediction accuracy for monitoring disease progression.
The paper tackled the problem of predicting Alzheimer's disease progression by introducing L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional transformation with a pre-trained Tabular Foundation Model, achieving state-of-the-art results in ventricular volume prediction on the TADPOLE dataset.
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.