系统梳理合成医疗表格数据评估的痛点与规范指南。
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
- 从134项研究中提炼评估方法的共性挑战
- 发现评估指标应用不一致、专家参与度低等问题
- 提供可复现的评估框架，适合数据合规与临床研究者
生成合成医疗表格数据极具挑战，其质量评估同样复杂甚至更难。本系统综述基于过去十年发表的2067篇相关论文，筛选出134项研究进行深入分析。研究揭示关键问题：评估方法缺乏共识、评估指标应用不一致、领域专家参与不足、数据集特征报告不全、结果可复现性差。为此，本文构建了合成数据生成与评估方法的分类体系，并提出实用指南，以推动更稳健、标准化的评估实践。目标是支持合成健康数据负责任地开发与使用，契合透明度、可复现性和治理的新期待，助力该技术潜力充分释放，加速医学创新。
原文摘要 · Abstract (English)
Generating synthetic tabular health data is challenging, and evaluating their quality is equally, if not more, complex. This systematic review highlights the critical importance of rigorous evaluation of synthetic health data to ensure reliability, clinical relevance, and appropriate use. From an initial identification of 2067 relevant papers published in the last ten years, 134 studies were selected for detailed analysis. Our review identifies key challenges, including lack of consensus on evaluation methods, inconsistent application of evaluation metrics, limited involvement of domain experts, inadequate reporting of dataset characteristics, and limited reproducibility of results. In response, we provide a structured consolidation of synthetic data generation and evaluation methods into taxonomies, alongside practical guidelines to support more robust and standardised evaluation practices. These findings aim to support the responsible development and use of synthetic health data, aligned with emerging expectations around transparency, reproducibility, and governance, ultimately enabling the community to fully harness its transformative potential and accelerate innovation.
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