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.
Lu Wang, Yan Li, and Mark Chignell
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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.