基于大语言模型生成的合成数据训练模型的临床通信处理：结构化综述与新型应用案例研究
Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studies
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中文总结 AI 辅助
本文综述用大语言模型生成的合成数据训练临床NLP系统的研究，结合13项无真实标注数据的案例，发现合成通信可支撑相关系统，微调编码器模型具竞争力，需真实数据迁移等完善。
中文摘要 AI 辅助
临床的诸多价值并非通过结构化记录传递，而是通过各类通信实现：患者描述症状、临床医生推理并给出指示、救护车向急诊科交接、护士交接班等场景中的交流。这类语言与表格数据不同，其含义取决于说话者角色、意图、因果关系、不确定性、信息遗漏及信道噪声。因此，医疗自然语言处理必须解读实际传递的信息，而非仅处理编码后的数据。这需要标注完善的语料库，但真实交流数据因隐私性、碎片化及标注成本高昂而稀缺。大语言模型提供了可行方案，可将临床来源（如记录、诊断标签、症状列表或护理计划）转化为书面及转录的通信内容，供下游模型使用。本文按源表示、通信形式与参与者、生成方法、下游任务组织了结构化叙述综述，并补充了13项新型案例研究。这些研究在无真实世界标注数据的情况下，构建了针对各类通信渠道和语言的临床NLP系统，包括院前急救医疗服务（EMS）报告、现场无线电伤亡记录、护士交接班、患者门户分诊及低资源环境下的出院沟通。结果表明，合成通信可为此类系统提供初始支撑。研究发现包括：微调后的编码器模型相较于评估的零样本基线具有竞争力，刻意降低通信质量对提升鲁棒性有价值。主要局限在于，多数研究在保留的合成通信上进行评估，而“训练用合成数据、测试用真实数据”的证据仍有限。本文结论为：合成临床通信正成为实用的研究资源；要将其确立为可复用的临床基础设施，还需真实数据迁移、安全性验证及外部验证。
英文摘要
Much clinical value is conveyed not through structured records but through communication: exchanges in which patients describe symptoms, clinicians reason and give instructions, ambulances hand over to emergency departments, and nurses pass on a shift. Such language differs from tabular data because meaning depends on speaker role, intent, causality, uncertainty, omission, and channel noise. Healthcare natural language processing must therefore interpret information as conveyed rather than coded. This requires well-annotated corpora, which are scarce because authentic exchanges are private, fragmented, and costly to annotate. Large language models offer a way forward by transforming clinical sources, such as records, diagnostic labels, symptom lists, or care plans, into written and transcribed communication for downstream models. We present a structured narrative survey organized by source representation, communication form and participants, generation method, and downstream task, complemented by thirteen novel case studies. These build clinical NLP systems for communication channels and languages without labeled real-world data, including EMS pre-arrival reports, field-radio casualty documentation, nurse handoffs, patient-portal triage, and low-resource discharge communication. They show that synthetic communication can bootstrap such systems. Findings include the competitiveness of fine-tuned encoder models over evaluated zero-shot baselines and the value of deliberately degraded communication for robustness. The main limitation is that most studies evaluate on held-out synthetic communication, while train-on-synthetic, test-on-authentic evidence remains limited. We conclude that syn-thetic clinical communication is becoming a practical research resource; establishing it as reusable clinical infrastructure will require authentic-data transfer, safety and external validation.
