meds_reader: A fast and efficient EHR processing library
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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
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025FoMoH: A clinically meaningful foundation model evaluation for structured electronic health recordscited by 6
- 2026PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Modelscited by 2
- 2026PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learningcited by 1
- 2026LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Modelscited by 0
- 2026LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models16 Jul 2026
- 2026PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models23 Jun 2026
- 2026PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning23 Jan 2026
- 2025FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records22 May 2025