https://github.com/mit-lcp/echo-data
Code related to extracting structured data from echocardiography reports
https://github.com/mit-lcp/echo-data
Last synced: 9 months ago
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Code related to extracting structured data from echocardiography reports
- Host: GitHub
- URL: https://github.com/mit-lcp/echo-data
- Owner: MIT-LCP
- License: mit
- Created: 2016-02-29T20:58:17.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2016-02-29T21:14:24.000Z (almost 10 years ago)
- Last Synced: 2025-02-15T19:51:16.811Z (11 months ago)
- Size: 1.95 KB
- Stars: 1
- Watchers: 7
- Forks: 3
- Open Issues: 2
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Structuring data from echocardiography reports
This repository contains code for structuring data from echocardiography reports.
This data can be used in studying the relationship between cardiac function and patient health.
# How to access the data
If you are simply interested in accessing the data, it is available on PhysioNetWorks: https://physionet.org/users/
PhysioNet Works is an online repository allowing selective access to data for credentialed users. In order to directly access the data, users must be accredited to use the MIMIC-III clinical database.
If you do not have the appropriate access, you can acquire it by following the steps detailed [here](http://mimic.physionet.org/gettingstarted/access/).
Once you have access to the MIMIC-III database, log in to PhysioNetWorks with your username/password.
Go to the "MIMIC-III Related Work" project, and scroll down to "Echocardiography data". The CSV files are available to download.
They can be linked back to the MIMIC-III database using `SUBJECT_ID`, `HADM_ID` or `ICUSTAY_ID`.
# How to use this repository
Before using this repository, you'll need to:
1. Install Python 2.7
* The numpy, pandas, and psycopg2 packages are also required
2. Install PostgreSQL 9.4 or higher
3. Install MIMIC-III locally on your system
* This requires access to the MIMIC-III database, detailed [here](http://mimic.physionet.org/gettingstarted/access/).
* An install guide is available [here](http://mimic.physionet.org/tutorials/install-mimic-locally-ubuntu/) (Ubuntu/Mac OS X) or [here](http://mimic.physionet.org/tutorials/install-mimic-locally-windows/) (Windows).
Once these are available on your system, you can begin running the Python script which will generate