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ehrqc

EHRQC

https://github.com/ryashpal/ehrqc

Science Score: 77.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: sciencedirect.com
  • ✓
    Committers with academic emails
    1 of 2 committers (50.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (15.8%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

EHRQC

Basic Info
  • Host: GitHub
  • Owner: ryashpal
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 39.2 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 3 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

EHRQC

Introduction

EHR-QC is a complete end-to-end pipeline to standardise and preprocess Electronic Health Records (EHR) for downstream integrative machine learning applications. This utility has two distinct modules;

  1. Standardisation
  2. Pre-processing

Both the modules can be run as a single end-to-end pipeline or individual components can be run in a standalone manner.

This utility is primarily focussed to provide a domain specific toolset for performing commmon standardisation and pre-processing tasks while handling the healthcare data. A command line interface is designed to provide an abstraction over the internal implementation details while at the same time being easy to use for anyone with basic Linux skills.

Workflow

ehr-qc

Quick Start Guide

Clone the repository from GitHub.

shell git clone git@github.com:ryashpal/EHRQC.git

Create a python virtual environment and activate it

shell python -m venv .venv source .venv/bin/activate

Install the required dependencies

shell pip install -r requirements.txt

Documentation

For the most up-to-date documentation about installation, configuration, running, and use cases please refer to the EHR-QC Documantation page.

Cite Us

If you use this library for your research, please cite our paper:

Yashpal Ramakrishnaiah, Nenad Macesic, Geoffrey I. Webb, Anton Y. Peleg, Sonika Tyagi,

EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes, Journal of Biomedical Informatics,

Volume 147, 2023, 104509, ISSN 1532-0464,

https://doi.org/10.1016/j.jbi.2023.104509.

(https://www.sciencedirect.com/science/article/pii/S1532046423002307)

BibTeX:

@article{RAMAKRISHNAIAH2023104509, title = {EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes}, journal = {Journal of Biomedical Informatics}, volume = {147}, pages = {104509}, year = {2023}, issn = {1532-0464}, doi = {https://doi.org/10.1016/j.jbi.2023.104509}, url = {https://www.sciencedirect.com/science/article/pii/S1532046423002307}, author = {Yashpal Ramakrishnaiah and Nenad Macesic and Geoffrey I. Webb and Anton Y. Peleg and Sonika Tyagi}, keywords = {Digital health, Electronic health records, EHR, Clinical outcome prediction, Machine learning}, }

Acknowledgements

Our special thanks to;

the-alfred-hospital-logo the-alfred-hospital-logo Superbug_AI_Branding_FINAL

Owner

  • Login: ryashpal
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: If you use this software, please cite the below article.
authors:
  - family-names: Yashpal Ramakrishnaiah
    given-names: Nenad Macesic
  - family-names: Geoffrey I. Webb
    given-names: Anton Y. Peleg
title: 'EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes'
version: 1.0.0
url: https://www.sciencedirect.com/science/article/pii/S1532046423002307
doi: https://doi.org/10.1016/j.jbi.2023.104509
date-released: '2023-12-04'
preferred-citation:
  authors:
    - family-names: Yashpal Ramakrishnaiah
      given-names: Nenad Macesic
    - family-names: Geoffrey I. Webb
      given-names: Anton Y. Peleg
  title: 'EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes'
  doi: https://doi.org/10.1016/j.jbi.2023.104509
  url: https://www.sciencedirect.com/science/article/pii/S1532046423002307
  type: article-journal
  pages: '104509'
  year: '2023'
  conference: {}
  publisher: {}

GitHub Events

Total
  • Push event: 6
Last Year
  • Push event: 6

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 31
  • Total Committers: 2
  • Avg Commits per committer: 15.5
  • Development Distribution Score (DDS): 0.097
Top Committers
Name Email Commits
Yashpal Ramakrishnaiah y****1@m****u 28
ryashpal 5****l@u****m 3
Committer Domains (Top 20 + Academic)
monash.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 13 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 4
  • Total maintainers: 1
pypi.org: ehrqc

Package for performing QC on Electronic Health Record (EHR) data

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 13 Last month
Rankings
Dependent packages count: 10.1%
Dependent repos count: 21.6%
Forks count: 29.8%
Average: 32.4%
Stargazers count: 38.8%
Downloads: 61.8%
Maintainers (1)
ryashpal
Last synced: 6 months ago

Dependencies

.github/workflows/gitlab_sync.yml actions
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  • wangchucheng/git-repo-sync v0.1.0 composite
.github/workflows/main.yml actions
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Dockerfile docker
  • python 3.9-slim build
docker-compose.yml docker
  • ehr-qc latest
notebooks/requirements.txt pypi
  • Babel ==2.9.1
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  • scikit-learn ==1.0
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requirements.txt pypi
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  • ipython ==8.8.0
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  • nvidia-cuda-runtime-cu11 ==11.7.99
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