Auxiliary modalities add no detectable discrimination or calibration over structured EHR for 30-day COPD readmission: evidence from a leakage-audited MIMIC-IV cohort

Background

Hospital readmission is a common and costly outcome in patients with chronic obstructive pulmonary disease (COPD). Multimodal deep learning models combining structured electronic health record (EHR) data with physiological, imaging, and textual data have been proposed to improve its prediction, but whether such fusion outperforms EHR data alone, once temporal-leakage safeguards are applied, remains unclear.