小模型经领域微调后可精准辅助急诊分诊，兼顾隐私与效率。
Domain-Adapted Small Language Models for Reliable Clinical Triage
- 用临床案例摘要提示小模型，提升分诊准确性。
- 微调后的Qwen2.5-7B模型误差显著降低，优于大模型。
- 适合医院定制化部署，保护患者隐私且计算成本低。
准确一致的急诊严重程度指数（ESI）分配在急诊科仍是难题，自由文本分诊记录差异大导致误分诊和流程低效。本研究评估开源小型语言模型（SLMs）能否作为可靠的隐私保护决策支持工具。通过系统比较多种SLM及提示策略，发现基于临床病例摘要的输入能带来最佳预测效果。其中，Qwen2.5-7B模型在准确性、稳定性与计算效率间表现最优。利用专家标注与银标准儿科分诊数据进行大规模领域自适应微调后，该模型显著降低分歧与临床关键错误，超越所有基线SLMs及先进商用大模型（如GPT-4o）。结果表明，机构定制的小型模型可用于可靠、隐私友好的ESI决策支持，强调针对性微调优于复杂推理策略。
原文摘要 · Abstract (English)
Accurate and consistent Emergency Severity Index (ESI) assignment remains a persistent challenge in emergency departments, where highly variable free-text triage documentation contributes to mistriage and workflow inefficiencies. This study evaluates whether open-source small language models (SLMs) can serve as reliable, privacy-preserving decision-support tools for clinical triage. We systematically compared multiple SLMs across diverse prompting pipelines and found that clinical vignettes, concise summaries of triage narratives, yielded the most accurate predictions. The SLM, Qwen2.5-7B, demonstrated the strongest balance of accuracy, stability, and computational efficiency. Through large-scale domain adaptation using expert-curated and silver-standard pediatric triage data, fine-tuned Qwen2.5-7B models substantially reduced discordance and clinically significant errors, outperforming all baseline SLMs and advanced proprietary large language models (LLMs, e.g., GPT-4o). These findings highlight the feasibility of institution-specific SLMs for reliable, privacy-preserving ESI decision support and underscore the importance of targeted fine-tuning over more complex inference strategies.
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