利用隐式神经微分方程从发病后稀疏测量值预测血凝块生长
Predicting blood clot growth from sparse post-onset measurements with latent neural differential equations
AI总结：
本研究基于隐式神经微分方程构建框架，结合稀疏血凝块测量数据推断未知参数并预测血栓生长，经七种方法对比，SNODE表现最优，为个性化血栓建模提供了新途径。
AI中文摘要：
凝血的计算模型有助于理解血栓形成，但其临床应用仍受限，因为许多模型输入难以测量且患者特异性数据往往稀疏。我们提出一种基于隐式神经微分方程的计算框架，可从稀疏测量值推断未知模型参数并预测血栓进展。我们利用多物理场血凝块模型生成的数据验证该框架，其中血凝块生长由凝血级联反应和扩散控制。我们使用四种已知生化输入（纤维蛋白原及因子IX、VIII、V），结合稀疏的早期血凝块大小观测值，推断组织因子参数并预测后续血凝块生长。我们对比了七种概率方法：随机神经常微分方程（SNODE）、随机神经泛函微分方程（SNFDE）、隐式神经过程基线、单调概率深度集成、经验轨迹检索、PCA-岭高斯后验及Gompertz曲线检索。SNODE在推断未知输入和预测未来血凝块生长轨迹方面表现最佳，SNFDE表现相似且始终优于其他非微分模型。预测准确性随观测值增多而提升，而预测 horizon 越长则不确定性增加、准确性降低。因此，隐式神经微分方程可有效结合稀疏测量下的参数推断与血凝块生长预测，为个性化血栓建模提供了有前景的基础。
英文摘要：
Computational models of blood clotting improve understanding of thrombus formation, but their clinical application remains limited because many model inputs are difficult to measure and patient-specific data are often sparse. We present a computational framework based on latent neural differential equations that infers unknown model parameters from sparse measurements and forecasts thrombosis progression. We demonstrate the framework using data generated from a multiphysics blood-clotting model in which clot growth is governed by the coagulation cascade and diffusion. Four known biochemical inputs (fibrinogen and factors IX, VIII, and V), together with sparse early clot-size observations, are used to infer the tissue-factor parameter and predict subsequent clot growth. We compare seven probabilistic methods: stochastic neural ordinary differential equations (SNODE), stochastic neural functional differential equations (SNFDE), a latent neural-process baseline, a monotone probabilistic deep ensemble, empirical trajectory retrieval, PCA-ridge Gaussian posterior, and Gompertz-curve retrieval. SNODE achieved the best performance in inferring the unknown input and forecasting future clot-growth trajectories. SNFDE performed similarly and consistently outperformed the other non-differential models. Prediction accuracy improved as more observations became available, whereas longer forecasting horizons increased uncertainty and decreased accuracy. Latent neural differential equations thus effectively combine parameter inference and clot-growth forecasting from sparse measurements, providing a promising foundation for personalized thrombosis modeling.
