Medication information extraction from clinical notes supports medication reconciliation, pharmacovigilance, and clinical decision support, helping reduce medication errors and preventable patient harm. However, the linguistic variability and contextual dependence make medication mentions and their associated attributes difficult to identify consistently. Human-annotated reference corpora are therefore important for system development and rigorous evaluation, yet remain scarce. MIMIC-IV-Note provides a large-scale collection of discharge summaries but lacks a human-annotated, medication-specific reference corpus. We present a corpus of 305 MIMIC-IV-Note discharge summaries annotated by four annotators using a written guideline and adjudicated into a single reference version. The corpus covers seven entity types, a three-valued medication-status attribute, and five medication–attribute relation types. Pairwise inter-annotator agreement reached an exact-span F-measure of 0.896 and a character-level F-measure of 0.927. Medication mentions are split between medication-list sections (55.4%) and narrative sections (44.6%), with attribute information concentrated in the list sections (2.86 versus 0.59 attribute types per mention). A supervised entity-recognition baseline achieved a micro-averaged exact-span F-measure of 0.945 against the adjudicated reference on the held-out test set. Error analysis showed that most remaining errors involved span-boundary disagreements, particularly for the reason and duration attributes. We plan to release the corpus through PhysioNet to support reproducible development and evaluation of medication information extraction systems on MIMIC-IV-Note and to provide a curated human reference for assessing automatically generated medication labels.

Mapping free-text mentions in clinical notes to standardized terminologies such as SNOMED CT is essential for large-scale secondary use of electronic health records, but remains challenging due to linguistic variability, under-specified annotation guidelines, term ambiguity, and ontology scale. This work presents a two-stage entity linking pipeline that combines span detection with context-aware concept linking and evaluates it on the SNOMED CT Entity Linking Challenge dataset. Our work builds upon the SNOMED CT entity linking challenge (CITATION), resulting in a fully open-source system. To our knowledge, this is the first end-to-end open-source system for this task. For span detection, we compare multiple neural architectures together with dictionary-based matching. For concept linking, we adopt a context-aware bi-encoder, and construct a multi-source knowledge base enriched with context derived from the SNOMED CT ontology. Finally, we implement an agentic re-ranker and test the effectiveness of LLM-backed re-ranking with access to annotation guidelines. In contrast to findings from the original shared task submissions, we show that context is important for optimal performance, and that agentic re-ranking with a state-of-the-art LLM only marginally improves overall performance, suggesting that the current benchmark may be approaching its practical ceiling. This work provides the first fully open-source, reproducible system for SNOMED CT entity linking, offering a foundation for future research and practical deployment.

This paper describes our submission for the BioNLP ACL 2025 Shared task on grounded Question Answering (QA) from Electronic Health Records (EHRs). The task aims to automatically generate answers to patients’ health related questions that are grounded in the evidence from their clinical notes. We propose a two stage retrieval pipeline to identify relevant sentences to guide response generation by a Large Language Model (LLM). Specifically, our approach uses a BioBERT based bi-encoder for initial retrieval, followed by a re-ranking step using a fine-tuned cross-encoder to enhance retrieval precision. The final set of selected sentences serve as an input to Mistral 7B model which generates answers through few-shot prompting. Our approach achieves an overall score of 31.6 on the test set, outperforming a substantially larger baseline model LLaMA 3.3 70B (30.7), which demonstrates the effectiveness of retrieval-augmented generation for grounded QA.