构建500份澳洲全科医疗笔记合成数据集，真实还原临床复杂性。
SynGP500: A Clinically-Grounded Synthetic Dataset of Australian General Practice Medical Notes
- 基于临床课程与流行病学数据生成，覆盖常见及罕见病症。
- 真实模拟医患沟通中的错别字、不完整记录等复杂情况。
- 适合开发和评估澳洲全科医疗NLP模型，隐私保护性强。
我们提出SynGP500，一个由临床医生精心构建的500份澳大利亚全科医疗笔记合成数据集。该数据集融合了RACGP 2022课程体系的临床广度、基于BEACH研究的流行病学校准发病率，以及多样化的就诊情境。此方法系统涵盖常见症状及课程要求识别但单个诊所中出现频率较低的疾病，相比自然分布受限的数据集，有助于提升模型的泛化能力。SynGP500刻意保持“混乱”设计，反映真实医疗实践的复杂性：包括简略记录、拼写错误、患者依从性差、社会经济障碍及医患分歧等，区别于过度净化的合成数据。多维度验证显示，该数据集在流行病学上与真实澳大利亚全科门诊模式高度一致（基于BEACH研究），风格分析证实语言多样性高，语义覆盖广，自监督医学概念提取的下游任务中F1值提升。SynGP500填补了国家级空白，为研究人员与教育者提供开发和评估澳大利亚全科医疗NLP方法的资源，同时天然保障患者隐私。
原文摘要 · Abstract (English)
We introduce SynGP500, a clinician-curated collection of 500 synthetic Australian general practice medical notes. The dataset integrates curriculum-based clinical breadth (RACGP 2022 Curriculum), epidemiologically-calibrated prevalence (BEACH study), and diverse consultation contexts. This approach systematically includes both common presentations and less-common curriculum-specified conditions that GPs must recognize but appear infrequently in single practice populations, potentially supporting more generalizable model training than datasets constrained by naturally occurring case distributions. SynGP500 is messy by design, reflecting the authentic complexity of healthcare delivery: telegraphic documentation, typos, patient non-adherence, socioeconomic barriers, and clinician-patient disagreements, unlike sanitized synthetic datasets that obscure clinical realities. Multi-faceted validation demonstrates dataset quality through epidemiological alignment with real Australian GP consultation patterns (BEACH study), stylometric analysis confirming high linguistic variation, semantic diversity analysis demonstrating broad coverage, and exploratory downstream evaluation using self-supervised medical concept extraction, showing F1 improvements. SynGP500 addresses a critical national gap, providing researchers and educators with a resource for developing and evaluating clinical NLP methods for Australian general practice while inherently protecting patient privacy.
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