DualAlign：生成具有临床基础的合成数据
DualAlign: Generating Clinically Grounded Synthetic Data
AI总结：
本文提出DualAlign框架，通过统计对齐和语义对齐生成具有临床基础的合成数据，结合真实症状轨迹提升数据保真度与合理性，在阿尔茨海默病案例中显著提升了LLaMA 3.1-8B模型的性能。
摘要：
鉴于真实世界EHR的严格隐私限制、标注罕见病数据的有限可用性以及观察性数据集中的系统性偏差，合成临床数据对于推进医疗保健中的AI日益重要。尽管大型语言模型（LLMs）能够生成流畅的临床文本，但生成既逼真又具临床意义的合成数据仍具挑战。我们引入了DualAlign框架，通过双重对齐增强统计保真度与临床合理性：（1）统计对齐，基于患者人口统计学和风险因素条件化生成；（2）语义对齐，结合真实世界症状轨迹指导内容生成。以阿尔茨海默病（AD）为案例，DualAlign生成具有上下文基础的症状级句子，更真实地反映真实世界的临床文档。使用DualAlign生成数据与人工标注数据组合微调LLaMA 3.1-8B模型，相比仅用黄金数据或无指导合成基线训练的模型，取得了显著的性能提升。尽管DualAlign未能完全捕捉纵向复杂性，但它提供了一种实用方法，用于生成具有临床基础且保护隐私的合成数据，以支持低资源临床文本分析。
英文摘要：
Synthetic clinical data are increasingly important for advancing AI in healthcare, given strict privacy constraints on real-world EHRs, limited availability of annotated rare-condition data, and systemic biases in observational datasets. While large language models (LLMs) can generate fluent clinical text, producing synthetic data that is both realistic and clinically meaningful remains challenging. We introduce DualAlign, a framework that enhances statistical fidelity and clinical plausibility through dual alignment: (1) statistical alignment, which conditions generation on patient demographics and risk factors; and (2) semantic alignment, which incorporates real-world symptom trajectories to guide content generation. Using Alzheimer's disease (AD) as a case study, DualAlign produces context-grounded symptom-level sentences that better reflect real-world clinical documentation. Fine-tuning an LLaMA 3.1-8B model with a combination of DualAlign-generated and human-annotated data yields substantial performance gains over models trained on gold data alone or unguided synthetic baselines. While DualAlign does not fully capture longitudinal complexity, it offers a practical approach for generating clinically grounded, privacy-preserving synthetic data to support low-resource clinical text analysis.
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。
