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Florida Institute of Technology

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Representative Papers

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

Aug 08, 2026

This study addresses the challenge of clinical thrombus modeling, which is hindered by sparse patient-specific data and the inaccessibility of key biochemical factors. It introduces, for the first time, latent-variable neural differential equations to model thrombus dynamics, leveraging sparse early-stage thrombus size measurements and partially known factors to jointly infer unknown tissue factor parameters and predict subsequent growth trajectories. The authors systematically evaluate seven probabilistic methods—including SNODE and SNFDE—on multiphysics coagulation simulation data. Results demonstrate that SNODE achieves the best performance in both parameter inference and dynamic prediction, with SNFDE ranking second and significantly outperforming non-differential models. Prediction accuracy improves with more observations yet degrades over longer forecasting horizons, underscoring the framework’s potential to overcome traditional models’ reliance on dense data.

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Latest Papers

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

Aug 08, 2026

This study addresses the challenge of clinical thrombus modeling, which is hindered by sparse patient-specific data and the inaccessibility of key biochemical factors. It introduces, for the first time, latent-variable neural differential equations to model thrombus dynamics, leveraging sparse early-stage thrombus size measurements and partially known factors to jointly infer unknown tissue factor parameters and predict subsequent growth trajectories. The authors systematically evaluate seven probabilistic methods—including SNODE and SNFDE—on multiphysics coagulation simulation data. Results demonstrate that SNODE achieves the best performance in both parameter inference and dynamic prediction, with SNFDE ranking second and significantly outperforming non-differential models. Prediction accuracy improves with more observations yet degrades over longer forecasting horizons, underscoring the framework’s potential to overcome traditional models’ reliance on dense data.

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