为欧洲健康数据空间做准备:一个开源编译器,用于快速,透明和可移植的健康数据转换
Stefan Beyer1,2, Nikola Tanjga1,2,3, Gabriel Kleinoscheg3
1Center for Health & Bioresources, AIT Austrian Institute of Technology, Vienna, Austria.
Frontiers in medicine
|September 29, 2025
概括
一个新的编译器,MaLaC-HD,将FHIR绘图语言 (FML) 翻译成Python,大大加速了诸如欧洲健康数据空间 (EHDS) 等倡议的健康数据转换. 这种工具比现有方法提供了近100倍的速度改进.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 医疗保健系统产生大量不同的,不兼容的数据.
- 互操作性和二次数据的使用对于像欧洲健康数据空间 (EHDS) 这样的倡议至关重要.
- 现有的用于FHIR映射语言 (FML) 执行的工具缺乏处理速度.
研究的目的:
- 为FML开发一个高效的编译器.
- 为了解决当前FML执行工具的性能限制.
- 为了促进健康数据的转换,以实现互操作性和二次使用.
主要方法:
- 开发了用于健康数据的映射语言编译器 (MaLaC-HD) 以将FML编译为Python.
- 通过使用一个大型ELGA文档,将MaLaC-HD的执行速度与现有的FML工具进行了比较.
- 对Java参考实现进行了符合性评估,并分析了生成的Python代码.
主要成果:
- 在考虑基线开销后,MaLaC-HD实现了比现有工具快近100倍的执行速度.
- 生成的Python代码与参考实现的输出密切匹配,并满足所有功能要求.
- 编译器证明了对复杂数据转换的能力.
结论:
- MaLaC-HD为健康数据转换提供了强大而高效的解决方案.
- 该工具可以支持二次数据的使用和大规模的健康数据研究.
- 马拉C-HD对于EHDS倡议的成功和EHR互操作性至关重要.
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