The Covid Symptom Study

This study uses NLP to identify symptoms of COVID-19 in clinical notes. Specifically, it uses cTAKES and cNLP transformers to identify clinical terms.

Preparation

Because it uses external services like cTAKES, you will want to make sure those are ready. From your git clone of the cumulus-etl repo, you can run the following to run those services:

export UMLS_API_KEY=your-umls-api-key  # don't forget to set this - cTAKES needs it
docker compose --profile covid-symptom-gpu up -d

You’ll notice the -gpu suffix there. Running NLP is much, much faster with access to a GPU, so we strongly recommend you run this on GPU-enabled hardware.

And since we are running the GPU profile, when you do run the ETL, you’ll want to launch the GPU mode instead of the default cumulus-etl CPU mode:

docker compose run cumulus-etl-gpu …

But if you can’t use a GPU or you just want to test things out, you can use --profile covid-symptom above and the normal cumulus-etl run line to use the CPU.

Task

There is one main task, run with --task covid_symptom__nlp_results.

This will need access to clinical notes, which are pulled fresh from your EHR (since the ETL doesn’t store clinical notes). This means you will likely have to provide some other FHIR authentication arguments like --smart-client-id and --fhir-url.

See --help for more authentication options.