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meds_reader: A fast and efficient EHR processing library

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arxiv 2409.09095 v2 pith:BLLMZAB5 submitted 2024-09-12 cs.LG cs.DB

Ethan Steinberg , Michael Wornow , Suhana Bedi , Jason Alan Fries , Matthew B. A. McDermott , Nigam H. Shah This is my paper · ORCID
classification cs.LGcs.DB
keywords medsreaderprocessingefficientdatapipelinesspeedachieving
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models

    cs.LG 2026-07 conditional novelty 6.0 of 10

    A pre-training method that aligns ICU time-series windows with LLM-encoded event summaries via a regularised InfoNCE loss improves downstream predictions and cross-dataset transfer.

  2. FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records

    cs.LG 2025-05 conditional novelty 5.0 of 10

    FoMoH benchmarks six structured EHR foundation models on 14 tasks and finds they do not consistently outperform supervised baselines, particularly for rare diseases and low-data regimes.

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