Clinical coding, assigning standardized codes to medical notes, is critical for epidemiological research, hospital planning, and reimbursement. Neural coding models generally process entire discharge summaries, which are often lengthy and contain information that is not relevant to coding. We propose an approach that combines Named Entity Recognition (NER) and Assertion Classification (AC) to filter for clinically important content before supervised code prediction. On MIMIC-IV, a standard evaluation dataset, our approach achieves near-equivalent performance to a state-of-the-art full-text baseline while using only 22% of the content and reducing training time by over half. Additionally, mapping model attention to complete entity spans yields coherent, clinically meaningful explanations, capturing coding-relevant modifiers such as acuity and laterality. We release a newly annotated NER+AC dataset for MIMIC-IV, designed specifically for ICD coding. Our entity-centric approach lays a foundation for more transparent and cost-effective assisted coding.

We describe Team MonoLink’s system for the ALTA 2025 Shared Task on normalizing patient-authored adverse drug event (ADE) mentions to MedDRA Lowest Level Terms (LLTs). Our pipeline combines recall-oriented, synonym-augmented candidate retrieval with cross-encoder re-ranking and a guideline-aware LLM discriminator. On the official hidden test set, our submission tied for first place, achieving an Accuracy@1 of 39.8%, Accuracy@5 of 78.3%, and Accuracy@10 of 85.5%.