Predicting blood clot growth from sparse post-onset measurements with latent neural differential equations

# Predicting blood clot growth from sparse post-onset measurements with latent neural differential equations
Source: [https://arxiv.org/abs/2608.08165](https://arxiv.org/abs/2608.08165)
[View PDF](https://arxiv.org/pdf/2608.08165)

> Abstract: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\.

## Submission history

From: Lennon Shikhman \[[view email](https://arxiv.org/show-email/4a2f1e44/2608.08165)\] **\[v1\]**Sat, 8 Aug 2026 14:47:28 UTC \(964 KB\)