用神经微分方程建模阿尔茨海默病生物标志物的连续演变过程。
ENC-ODE: Event-level Neurodegenerative Modeling in Continuous Time with Neural ODEs
- 基于神经微分方程，以诊断为条件建模事件间连续动态变化。
- 在ADNI数据集上，对多模态生物标志物预测精度优于主流序列模型。
- 适合临床辅助决策，尤其适用于稀疏不规则随访数据场景。
准确预测阿尔茨海默病等神经退行性疾病中临床生物标志物的时序演变，对早期诊断与管理至关重要。然而，这依赖于纵向数据来捕捉生物标志物随时间的变化，而这类数据常因成本高、耗时长及患者负担重而稀疏且不规则。为此，我们提出ENC-ODE：一种基于神经常微分方程的事件级连续时间神经退行性建模方法。ENC-ODE通过诊断条件下的连续动态建模临床事件，利用目标条件注意力机制，在无需历史压缩的情况下加权聚合事件级预测结果，以生成目标时间和模态的预测。在阿尔茨海默病影像学倡议（ADNI）数据集上的大量实验表明，ENC-ODE在多模态生物标志物预测任务中优于代表性序列模型，同时提供可扩展且具有神经科学基础的临床支持方案。代码已公开于https://github.com/JardinDelSol/enc-ode。
原文摘要 · Abstract (English)
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.
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