生成罕见病电子病历，助力机器学习算法测试。
SYNRARE: Synthetic Rare Disease EHR Generation for ML Benchmarking
- 基于Synthea构建图形界面，可定制生成与常见病患者差异可控的罕见病病历。
- 支持在真实医疗数据相似性下快速生成合成病历，用于算法性能对比。
- 适合医疗AI研究者用于模型基准测试，尤其关注罕见病诊断场景。
罕见病诊断常因症状与常见病相似而延迟。机器学习算法应用于电子健康记录（EHR）有望加速诊断，但法律与隐私问题构成障碍。合成数据生成是替代方案，可用于算法开发与基准测试。现有方法通常缺乏对罕见病患者群体与多数常见病患者差异程度的可控建模。本文提出SYNRARE，一个基于Synthea框架的图形化界面工具，支持灵活调整生成参数，创建与常见病患者仅在特定维度上存在差异的合成罕见病患者数据，实现受控条件下的算法测试与基准评估。该工具已开源，可通过https://gitlab.sdu.dk/screen4care/synrare获取安装说明。
原文摘要 · Abstract (English)
Motivation: Rare disease (RD) diagnosis is frequently delayed due to the similarities in symptoms to common disease variants. Machine Learning Algorithms applied to Electronic Health Records show promise for accelerating the diagnosis; however, legal and privacy concerns pose significant barriers. To address these issues, Synthetic Data Generation is an alternative method for obtaining Electronic Health Records and can be applied with any Machine Learning algorithm for benchmarking and development purposes. Despite the availability of Synthetic Data Generation algorithms, support for generating a subset of patients that differ in a definable degree from the majority to simulate patients with RD is often lacking. Results: We present SYNRARE, a graphical user interface based on the Synthea framework that enables easier modification and generation of synthetic Electronic Health Records of RD patients, which differ only to a definable degree from patients with common diseases, thereby enabling the benchmarking and testing of algorithms under controlled technical conditions. SYNRARE enables researchers to rapidly benchmark their Machine Learning algorithms across any scenario. Availability and implementation: SYNRARE, including detailed instructions for installing, is available at https://gitlab.sdu.dk/screen4care/synrare.
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