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arXiv Machine Learning By Jin-Jia Zheng, Vincent S. Tseng

Graph-Enhanced Disease Trajectory Discovery and Temporal Endpoint Forecasting from Longitudinal Electronic Health Records

Read the original on arXiv Machine Learning →

The study introduces a graph‑enhanced framework for discovering disease trajectories and forecasting temporal endpoints using longitudinal EHR data from MIMIC‑IV v3.1. It constructs patient‑specific motifs from consecutive admissions, applies a time‑aware Transformer to select up to 24 motifs per endpoint, and reports modest improvements in micro‑AUPRC and event‑time MAE, with larger gains for events occurring after 730 days. The authors also explore branching‑motif conditioning and GNN‑based edge repair, finding that motifs mainly provide long‑range timing support rather than superior endpoint discrimination or survival prediction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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