本地部署的小模型通过微调可实现急诊决策支持的临床竞争力。
Locally Deployable Small Language Models for Emergency Department Decision Support: A Systematic Benchmark of Fine-Tuning Strategies
- 用LoRA等策略微调开源小模型，实现本地化部署
- 在分诊和转诊任务上超越商用模型，诊断仍具挑战
- 能发现商用模型遗漏的重症患者，适合急诊场景
将大语言模型用于急诊科决策支持面临两大挑战：向封闭式商业模型传输患者数据存在隐私风险，且缺乏对本地部署开源小语言模型（SLMs）微调策略的系统评估。本研究在三个急诊任务上对比了八种开源SLMs，采用零样本提示、前缀微调、低秩适应（LoRA）和全量微调方法，使用2,083例MIMIC-IV-ED病例，并以Claude Haiku 4.5和Claude Sonnet 4.5为基准。结果表明，经LoRA微调的开源SLMs在分诊等级预测和专科转诊推荐任务上表现优于商用基线，而诊断预测仍具挑战；混淆矩阵分析显示，微调后的开源模型可识别出商用模型遗漏的高危患者。这些结果证明，本地部署的SLMs可在急诊决策支持中达到临床可用水平。
原文摘要 · Abstract (English)
Deploying large language models (LLMs) for decision support in emergency departments (EDs) faces two major challenges: privacy risks of transmitting patient data to closed-source commercial LLMs and the lack of systematic evaluation of fine-tuning strategies for locally deployable open-source small language models (SLMs). We benchmarked eight open-source SLMs using zero-shot prompting, prefix tuning, Low-Rank Adaptation (LoRA), and full fine-tuning on three ED tasks: triage level prediction, specialist referral recommendation, and diagnosis prediction. Using 2,083 MIMIC-IV-ED cases and Claude Haiku 4.5 and Claude Sonnet 4.5 as baselines, we found that LoRA fine-tuned open-source SLMs outperform commercial baselines on triage level prediction and specialist referral recommendation, while diagnosis prediction remains challenging for open-source SLMs. Confusion matrix analysis further shows that fine-tuned open-source SLMs can detect highest-severity patients missed by the commercial baselines. These results demonstrate that locally deployable SLMs can achieve clinically competitive performance for ED decision support.
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