# Next-Visit Diagnosis Prediction

The work proposes CARing, which compresses disease names and ICD ontology paths into compositional medical Semantic IDs (SIDs) via residual quantization, grounds them through six alignment tasks plus teacher-generated semantic-enrichment corpora and reasoning activation so a single autoregressive model reasons before emitting a diagnosis SID, and optimizes multi-label coverage with a coverage reward and multi-positive supervision; on MIMIC-III and MIMIC-IV it exceeds all EHR-trained baselines in weighted score and attains the highest recall at every reported cutoff, with R@30 of 46.04% and 46.52% in reasoning mode.

The work proposes CARing, which compresses disease names and ICD ontology paths into compositional medical Semantic IDs (SIDs) via residual quantization, grounds them through six alignment tasks plus teacher-generated semantic-enrichment corpora and reasoning activation so a single autoregressive model reasons before emitting a diagnosis SID, and optimizes multi-label coverage with a coverage reward and multi-positive supervision; on MIMIC-III and MIMIC-IV it exceeds all EHR-trained baselines in weighted score and attains the highest recall at every reported cutoff, with R@30 of 46.04% and 46.52% in reasoning mode.