meds_reader: A fast and efficient EHR processing library
This addresses the need for faster and more scalable EHR data processing for healthcare machine learning applications, though it is incremental as it builds on existing pipelines.
The paper tackles the problem of inefficient and unscalable processing of large electronic health record (EHR) datasets by introducing meds_reader, an optimized Python package that achieves 10-100x improvements in memory, speed, and disk usage.
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.