# meds_reader: A fast and efficient EHR processing library

arXiv: https://arxiv.org/abs/2409.09095 · HTML: https://mlsanity.com/paper/2409.09095/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 11 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 3 of 3)

- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events](https://mlsanity.com/paper/2306.11547/) (2023)
- [An Extensive Data Processing Pipeline for MIMIC-IV](https://mlsanity.com/paper/2204.13841/) (2022)

## Built on by (most cited first, 4 of 4)

- [FoMoH: A clinically meaningful foundation model evaluation for structured electronic health records](https://mlsanity.com/paper/2505.16941/) (2025)
- [PyHealth 2.0: A Comprehensive Open-Source Toolkit for Accessible and Reproducible Clinical Deep Learning](https://mlsanity.com/paper/2601.16414/) (2026)
- [PORTER: Language-Grounded Event Representations for Portable Structured EHR Foundation Models](https://mlsanity.com/paper/2606.24102/) (2026)
- [LLM4EHR: Aligning Clinical Time Series with Medical Event Sequences via Large Language Models](https://mlsanity.com/paper/2607.15447/) (2026)

## 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.