ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs
For clinicians and researchers, it improves early diagnosis and management of Alzheimer's by accurately forecasting biomarker trajectories from limited longitudinal data.
ENC-ODE uses neural ODEs to model continuous-time biomarker evolution from sparse, irregular clinical events, outperforming sequence models on the ADNI dataset for Alzheimer's disease prediction.
Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such as Alzheimer's disease. However, this relies on longitudinal data to capture biomarker changes over time, which is often sparse and irregular due to the high cost, labor-intensive nature, and patient burden. To address these challenges, we propose ENC-ODE, an Event-level Neurodegenerative modeling in Continuous time with neural Ordinary Differential Equations. ENC-ODE predicts future biomarker evolution by modeling clinical events through diagnosis-conditioned continuous dynamics. A target-conditioned attention mechanism weights and aggregates event-level predictions for the target time and modality without history compression. Extensive experiments on Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset demonstrate that ENC-ODE outperforms representative sequence models while offering a scalable and neuroscientifically grounded solution for clinical support. The code is available at https://github.com/JardinDelSol/enc-ode.