迈向多学科住院总结：高效的句子级临床来源分类
Towards Multidisciplinary Summarization of Hospital Stays: Efficient Sentence-Level Clinical Provenance Categorization
AI总结：
本研究提出一个基于大语言模型监督微调的临床来源分类流水线，用于多学科住院总结，通过量化70B模型在跨领域迁移中提升F1分数7%，并证明模型容量对语义灵活性的关键作用。
摘要：
在新生儿重症监护室（NICU）等高复杂性环境中，有效的“全团队”总结需要聚合来自不同学科（医生、护士、治疗师）的见解，这些见解分散在数百份临床自由文本记录中。简单地将异质文本汇集在一起往往会导致输出不连贯。因此，结构化总结首先需要对跨多源记录的句子级来源进行准确分类。本初步研究引入了一个临床来源分类流水线，使用大语言模型（LLM）的监督微调（SFT）。我们将两个Llama-3模型（8B和70B）适配到MedSecId，这是一个包含2,002份MIMIC-III（成人ICU）记录并带有临床来源标题注释的语料库，两个模型在域内均实现了超过92%的宏F1分数。为了评估跨域泛化能力，我们评估了模型容量（8B vs. 70B）和量化在一个由来自三个多学科NICU总结的227个句子级跨度组成的金标准数据集上的表现。实验结果表明存在规模依赖的迁移效应：虽然SFT对8B模型仅产生边际变化，但显著改进了70B模型，使宏F1提高了7%。值得注意的是，量化微调的70B模型在显著降低计算需求的同时，超越了其全精度基线。这些发现表明，足够的模型容量对于在跨域临床迁移中保持语义灵活性至关重要，并且高效的量化适配可以为下游总结实现结构化来源建模。
英文摘要：
Effective "all-team" summarization in high-complexity settings like the Neonatal Intensive Care Unit (NICU) requires aggregating insights from diverse disciplines (physicians, nurses, therapists) spread across hundreds of clinical free-text notes. Simply pooling heterogeneous text often leads to incoherent outputs. Structured summarization therefore first requires accurate categorization of sentence-level provenance across multi-source notes. This pilot study introduces a clinical provenance categorization pipeline using supervised fine-tuning (SFT) of large language models (LLMs). We adapted two Llama-3 models (8B and 70B) to MedSecId, a corpus of 2,002 MIMIC-III (Adult ICU) notes annotated with clinical provenance headers, achieving in-domain Macro F1 scores above 92% for both models. To evaluate cross-domain generalization, we assessed model capacity (8B vs. 70B) and quantization on a gold-standard dataset of 227 sentence-level spans derived from three multi-disciplinary NICU summaries. Experimental results demonstrate a scale-dependent transfer effect: while SFT produced only marginal changes for the 8B model, it substantially improved the 70B model, increasing Macro F1 by 7%. Notably, the quantized fine-tuned 70B model outperformed its full-precision baseline while substantially reducing computational requirements. These findings suggest that sufficient model capacity is critical for preserving semantic flexibility during cross-domain clinical transfer and that efficient quantized adaptation can enable structured provenance modeling for downstream summarization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
