Selection-based prompting with synthesized candidates unlocks high-performance clinical abbreviation disambiguation via open-source LLMs.

Abstract

Clinical abbreviations introduce semantic ambiguity that hinders automated understanding in healthcare informatics. While generative large language models (LLMs) show promise, direct generation often lacks clinical precision. We present S-ACAD, a task-specific framework that shifts the paradigm from generation to constrained selection using dual-pathway candidate construction to improve clinical abbreviation disambiguation. The S-ACAD pipeline consists of four stages: (i) Anchor generation via preliminary LLM expansion; (ii) Phrase-level retrieval of the top-3 semantic candidates; (iii) Contextual prototyping using pseudo-texts to identify three additional candidates; and (iv) Discriminative selection from the resulting 7-option pool. We evaluated the framework on the original and Adams's denoised CASI datasets using six open-source LLMs. S-ACAD demonstrates superior performance, with Gemma-2-9B-IT emerging as the best-performing model, achieving remarkable results on Adams's denoised CASI dataset (accuracy: 0.9228, Macro-F1: 0.9279). Even on the challenging original dataset, it maintains high consistency (accuracy: 0.7585, Macro-F1: 0.7585). Ablation studies confirm that shifting the paradigm from generation to selection via dual-pathway candidate construction is the primary driver of performance, significantly outperforming direct generative baselines. S-ACAD reframes clinical abbreviation disambiguation as a constrained selection task for generative LLMs, effectively unlocking the reasoning potential of small models through dual-pathway candidate construction. This offers a high-precision, economical, and privacy-preserving solution for standardizing electronic health records, thereby enhancing their downstream utility in clinical informatics.