自动稀疏化混合神经微分方程，提升医疗场景建模效率与稳定性。
Automatic and Structure-Aware Sparsification of Hybrid Neural ODEs
- 结合领域知识图修改与数据驱动正则化，自动筛选状态并优化结构。
- 在合成与真实数据上实现更高预测性能与鲁棒性，保持理想稀疏度。
- 适合医疗健康等数据稀缺场景下的混合模型降维与可解释性需求。
混合神经常微分方程（neural ODEs）融合机制模型与神经网络，具备强归纳偏置和灵活性，尤其适用于数据稀缺的医疗场景。然而，机制模型带来的过多隐状态与交互会导致训练效率低下和过拟合，限制其实际应用。为此，我们提出一种新的混合建模流程，通过结合领域先验的图结构修改与数据驱动的正则化方法，自动进行状态选择与结构优化，实现模型稀疏化，在提升预测性能与稳定性的同时保留机制合理性。在合成数据和真实世界数据上的实验表明，该方法实现了更优的预测表现与鲁棒性，并达到期望的稀疏程度，为医疗应用中的混合模型降维提供了有效解决方案。
原文摘要 · Abstract (English)
Hybrid neural ordinary differential equations (neural ODEs) integrate mechanistic models with neural ODEs, offering strong inductive bias and flexibility, and are particularly advantageous in data-scarce healthcare settings. However, excessive latent states and interactions from mechanistic models can lead to training inefficiency and over-fitting, limiting practical effectiveness of hybrid neural ODEs. In response, we propose a new hybrid pipeline for automatic state selection and structure optimization in mechanistic neural ODEs, combining domain-informed graph modifications with data-driven regularization to sparsify the model for improving predictive performance and stability while retaining mechanistic plausibility. Experiments on synthetic and real-world data show improved predictive performance and robustness with desired sparsity, establishing an effective solution for hybrid model reduction in healthcare applications.
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