mlsanityData to 7 Oct 2026

meds_reader: A fast and efficient EHR processing library

arXiv 2409.09095 ↗First posted 12 Sep 2024cs.LG cs.DBcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 3 of its 11 references that are in the map. First posted on arXiv 12 Sep 2024.

Builds on

3 of 11 references.

References · 3

  1. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  2. 2023Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Eventscited by 46
  3. 2022An Extensive Data Processing Pipeline for MIMIC-IVcited by 39

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.