TrajSurv learns continuous latent patient trajectories from irregular EHR data using an NCDE, aligns them with SOFA severity scores via time-aware contrastive learning, and uses vector-field and trajectory-clustering analyses to explain survival predictions.
Dynamical Survival Analysis with Controlled Latent States
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abstract
We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel modelization approach in which the intensity is the solution to a controlled differential equation. We first design a neural estimator by building on neural controlled differential equations. In a second time, we show that our model can be linearized in the signature space under sufficient regularity conditions, yielding a signature-based estimator which we call CoxSig. We provide theoretical learning guarantees for both estimators, before showcasing the performance of our models on a vast array of simulated and real-world datasets from finance, predictive maintenance and food supply chain management.
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TrajSurv: Learning Continuous Latent Trajectories from Electronic Health Records for Trustworthy Survival Prediction
TrajSurv learns continuous latent patient trajectories from irregular EHR data using an NCDE, aligns them with SOFA severity scores via time-aware contrastive learning, and uses vector-field and trajectory-clustering analyses to explain survival predictions.