curate-mimic - A repository to guide in demonstrating how to curate the MIMIC III database with Natural Language Processing

0. Preliminary steps:

   • Obtain access to the MIMIC III database (ask your PI).
   • Obtain a UMLS account and API key
   • Install Docker and docker-compose
   • If writing to a MongoDB database:
     • Create local directory: mkdir mimic_db
     • docker run --rm --name mongodb -d -v mimic_db:/data/db -p 27017:27017 mongo

1. Setup cTAKES containers:

   • git clone git@github.com:Machine-Learning-for-Medical-Language/ctakes-rest-package.git
   • cd ctakes-rest-package
   • export umls_api_key=<api key from above>
   • docker-compose up -d --scale ctakes=N # This starts N containers -- each requires around 4 GB RAM.

2. Run the python script to process the data -- run with -h to receive detailed documentation of the options:

   • python process_mimic.py --input-path <path to NOTEEVENTS.csv file> --output-format <json|mongo|xmi|fhir> --output_dir <directory to write files if output format is file-based>
   • If you run with the flag --max-notes N, you can run on a subset to make sure everything is working correctly before processing the whole dataset.

3. If you used MongoDB, check into some of the output:

   $ mongo

   > use mimic

   > db.note_nlp.stats()['count'] # Should return number of notes processed and entered into database