MIDAS_SYNUR at MEDIQA-SYNUR 2026: A Prompting Study for Clinical Observation Extraction from Nurse Dictation Transcriptions
Year: 2026
Type: conference-paper
Abstract: This paper describes MIDAS_SYNUR, a system developed for the MEDIQA-SYNUR task at ClinicalNLP 2026 on observation extraction from nurse dictations.The primary system adopts a single-prompt, field-rich few-shot strategy using GPT-5.2,jointly generating all schema fields in one structured output.Few-shot demonstrations are curated and grouped by value type, with five examples per type, promoting consistency across heterogeneous value distributions while leveraging global context to resolve cross-field dependencies.To analyze design trade-offs, this holistic strategy is compared against a field-wise decomposed prompting baseline, where each schema field is extracted independently using explicit positive and NULL demonstrations to improve absence detection and reduce cross-field interference.Zero-shot variants of both approaches are also evaluated to isolate the contribution of in-context examples.The results highlight inference-time prompting as a simple, reproducible, and competitive baseline for large-scale clinical observation extraction from conversational nurse dictations.
Authors: Swetha Krishna Sriram, Akshitaa Sahoo
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166571734