meds_reader加速电子病历数据处理，性能提升10-100倍。
meds_reader: A fast and efficient EHR processing library
- 利用电子病历固有特性优化数据处理流程
- 在内存、速度和磁盘使用上实现10-100倍改进
- 适合需要高效处理大规模EHR数据的研究者
医疗领域机器学习对电子病历（EHR）数据处理的需求日益增长，但现有流程在计算效率和可扩展性方面表现不佳。本文提出meds_reader，一个专为高效EHR数据处理设计的优化Python库，充分利用EHR数据的内在特性以提升处理速度。通过重实现两个主流EHR处理流水线的关键组件，验证了其显著优势：在内存占用、运行速度和磁盘使用上均实现10至100倍的提升。相关代码已开源，地址为https://github.com/som-shahlab/meds_reader。
原文摘要 · Abstract (English)
The growing demand for machine learning in healthcare requires processing increasingly large electronic health record (EHR) datasets, but existing pipelines are not computationally efficient or scalable. In this paper, we introduce meds_reader, an optimized Python package for efficient EHR data processing that is designed to take advantage of many intrinsic properties of EHR data for improved speed. We then demonstrate the benefits of meds_reader by reimplementing key components of two major EHR processing pipelines, achieving 10-100x improvements in memory, speed, and disk usage. The code for meds_reader can be found at https://github.com/som-shahlab/meds_reader.
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