Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness

ArXi:2604.21235v1 Announce Type: new Multimodal clinical records contain structured measurements and clinical notes recorded over time, offering rich temporal information about the evolution of patient health. Yet these observations are sparse, and whether they are recorded depends on the patient's latent condition. Observation patterns also differ across modalities, as structured measurements and clinical notes arise under distinct recording processes.