Summary of Meds_reader: a Fast and Efficient Ehr Processing Library, by Ethan Steinberg et al.
meds_reader: A fast and efficient EHR processing library
by Ethan Steinberg, Michael Wornow, Suhana Bedi, Jason Alan Fries, Matthew B. A. McDermott, Nigam H. Shah
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Databases (cs.DB)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces meds_reader, an optimized Python package for efficient electronic health record (EHR) data processing. By leveraging the intrinsic properties of EHR data, the package achieves significant improvements in memory, speed, and disk usage, outperforming existing pipelines by 10-100x. The authors demonstrate the benefits of meds_reader by reimplementing key components of two major EHR processing pipelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Meds_reader is a new tool that helps doctors and hospitals process big amounts of patient data quickly and efficiently. Right now, there are not many ways to do this well, so the team created a special package in Python called meds_reader. This package takes advantage of how medical data is structured to make it run faster and use less computer resources. The authors show that using meds_reader can make a big difference by reworking two important medical data processing systems. |
