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arXiv Machine Learning By Lennon J. Shikhman, Ying Qian, He Li

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

Read the original on arXiv Machine Learning →

arXiv:2608. 08165v1 Announce Type: new 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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