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metadata 
title: MediCode
sdk: docker
pinned: false

Medicode

Getting started

Prerequisites

  1. ffmpeg for audio processing in Bumblebee's speech-to-text serving: brew install ffmpeg.
  2. Postgres and pgvector for storing data and vector embeddings: brew install pgvector.

Running the server

To start your Phoenix server:

  • Run mix setup to install and setup dependencies
  • Run mix build_code_vectors to download the ICD-9 codelist, precompute vectors, and store the results in the database.
  • PDF generation is managed with ChromicPDF which assumes an executable is available at these locations. If you are having trouble getting Chrome to launch, this StackExchange was useful.
  • Start Phoenix endpoint with mix phx.server or inside IEx with iex -S mix phx.server

Now you can visit localhost:4000 from your browser.

Ready to run in production? Please check our deployment guides.

Deployment

The app is configured to deploy to Fly.io via a fly.toml file. To deploy, run fly deploy within the app's directory.

Precomputing code vectors

To build the code vectors for the ICD-9 codelist for the deployed environment:

  1. Connect to the server with fly ssh console.
  2. Run /app/bin/medicode eval Medicode.Release.precompute_code_vectors. This will prepare the vectors in the database if they are not present.

Livebook

In addition to connecting to the deployed application via iex, Livebook supports connecting to the running application. Connecting a Livebook instance to the deployed application involves the following:

  1. Install and setup Wireguard with a peer connection for Fly.io: Step by Step
  2. Install and start Livebook: Livebook.dev
  3. Connecting Livebook to a Production App requires a node name and cookie value:
  • Node name: medicode@myipfromfly ("myipfromfly" can be retrieved with fly ips private --app medical-transcription-cpu)
  • Cookie value: 0gfxcPtwryKxI2O1N0eFAg9p4MJGC-oUGShgj_wgvNEGiba5EDEJFA== (this value is set in fly.toml)

Run in Docker

  1. Create a local volume: docker volume create ml-data
  2. Ensure the volume is writeable: docker run --rm -v ml-data:/data busybox /bin/sh -c 'touch /data/.initialized && chmod 1777 /data'. More background:
  1. Build the image with: docker build . -t headwayio/medicode
  2. Run a container with: docker run --env-file ./.env -p 4000:4000 headwayio/medicode

Caveats

You may need to make a few changes to get the app running in Docker at the moment:

  • In lib/medicode/application.ex, comment out the DNSCluster child spec.
  • In rel/env.sh.eex, comment out the ERL_AFLAGS, RELEASE_DISTRIBUTION, and RELEASE_NODE environment variables.

Learn more