Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/aphp/eds-scikit
eds-scikit is a Python library providing tools to process and analyse OMOP data
https://github.com/aphp/eds-scikit
clinical-data-warehouse data-science medical omop python
Last synced: 3 months ago
JSON representation
eds-scikit is a Python library providing tools to process and analyse OMOP data
- Host: GitHub
- URL: https://github.com/aphp/eds-scikit
- Owner: aphp
- License: bsd-3-clause
- Created: 2022-11-28T12:57:23.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-04-12T14:14:13.000Z (10 months ago)
- Last Synced: 2024-04-12T22:13:26.347Z (10 months ago)
- Topics: clinical-data-warehouse, data-science, medical, omop, python
- Language: Python
- Homepage: https://aphp.github.io/eds-scikit
- Size: 13.8 MB
- Stars: 31
- Watchers: 1
- Forks: 5
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Changelog: changelog.md
- Contributing: contributing.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
#
[](https://aphp.github.io/eds-scikit/latest/)
[](https://pypi.org/project/eds-scikit/)
[](https://www.python.org/)
[]([https://www.python.org/](https://github.com/psf/black))
[](https://raw.githubusercontent.com/aphp/eds-scikit/coverage/coverage.txt)
[](https://zenodo.org/badge/latestdoi/571584236&style=flat-square)
eds-scikit is a tool to assist data scientists working on the AP-HP's Clinical Data Warehouse. It is specifically targeted for OMOP-standardized data. It main goals are to:
- Ease access and analysis of data
- Allow a better transfer of knowledge between projects
- Improve research reproducibility
## Development
This library is developed and maintained by the core team of AP-HP’s Clinical Data Warehouse (EDS) with the strong support of [Inria's SODA team](https://team.inria.fr/soda/).
## How to use
Please check the [online documentation](https://aphp.github.io/eds-scikit/) for more informations. You will find
- Detailed explanation of the project goal and working principles
- A complete API documentation
- Various Jupyter Notebooks describing how to use various functionnalities of eds-scikit
- And more !
## Requirements
eds-scikit stands on the shoulders of [Spark 2.4](https://spark.apache.org/docs/2.4.8/index.html) which requires:
- Python ~3.7.1
- Java 8
## Installation
You can install eds-scikit via `pip`:
```bash
pip install "eds-scikit[aphp]"
```
:warning: If you get an an error during installation, please try downgrading pip via `pip install -U "pip<23" before install `eds-scikit`
:warning: If you don't work in AP-HP's ecosystem (EDS), please install via:
```bash
pip install eds-scikit
```
You can now import the library via
```python
import eds_scikit
```
### Contributing
- You want to help on the project ?
- You developped an interesting feature and you think it could benefit other by being integrated in the library ?
- You found a bug ?
- You have a question about the library ?
- ...
Please check our [contributing guidelines](https://aphp.github.io/eds-scikit/contributing/).
### Citation
If you use `eds-scikit`, please cite us as below.
```bibtex
@misc{eds-scikit,
author = {Petit-Jean, Thomas and Remaki, Adam and Maladière, Vincent and Varoquaux, Gaël and Bey, Romain},
doi = {10.5281/zenodo.7401549},
title = {eds-scikit: data analysis on OMOP databases},
url = {https://github.com/aphp/eds-scikit}
}
```
### Acknowledgment
We would like to thank the following funders:
- [Assistance Publique – Hôpitaux de Paris](https://www.aphp.fr/)
- [AP-HP Foundation](https://fondationrechercheaphp.fr/)
- [Inria](https://www.inria.fr)