Retrieval-Augmented Framework Reconstructs Clinical Timelines from Text and EHR Data

A research paper on arXiv (2605.15168) introduces a retrieval-augmented multimodal alignment framework that reconstructs precise clinical timelines by combining unstructured clinical narratives with structured electronic health record (EHR) data. The framework addresses the limitations of each data type: narratives lack temporal precision, while EHR data misses clinically meaningful events. The approach formulates timeline reconstruction as a graph-based multistep process, extracting anchor events from narratives to build a temporal scaffold, placing non-central events relative to this backbone, and then calibrating the timeline. This method aims to improve temporal precision for modeling patient trajectories and forecasting risk in complex conditions like sepsis.