meds_reader: A fast and efficient EHR processing library

Fuente: arXiv
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Main Authors: Steinberg, Ethan, Wornow, Michael, Bedi, Suhana, Fries, Jason Alan, McDermott, Matthew B. A., Shah, Nigam H.
Format: Preprint
Published: 2024
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author Steinberg, Ethan
Wornow, Michael
Bedi, Suhana
Fries, Jason Alan
McDermott, Matthew B. A.
Shah, Nigam H.
author_facet Steinberg, Ethan
Wornow, Michael
Bedi, Suhana
Fries, Jason Alan
McDermott, Matthew B. A.
Shah, Nigam H.
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2409_09095
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle meds_reader: A fast and efficient EHR processing library
Steinberg, Ethan
Wornow, Michael
Bedi, Suhana
Fries, Jason Alan
McDermott, Matthew B. A.
Shah, Nigam H.
Machine Learning
Databases
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.
title meds_reader: A fast and efficient EHR processing library
topic Machine Learning
Databases
url https://arxiv.org/abs/2409.09095