Skip to content

Choose your path

MedDeID is a suite, but most users need only a small part of it. Start from the outcome you want.

All Python components install from PyPI. For inference, install meddeid or use the published GHCR image. The browser applications also have versioned public GHCR images, so ordinary users do not need Node.js or a source checkout.

Your goal Begin with Add only when needed
De-identify one note or a batch of notes meddeid meddeid[server] for an internal web service
Import hospital data for review meddeid-data meddeid for model pre-annotations
Review and correct PII spans with one annotator meddeid-annotate meddeid for optional model pre-annotations
Reconcile multiple reviewers meddeid-curate Use only when the study protocol requires it
Create a detailed evaluation benchmark meddeid-subannotate Start from completed reviewed annotations
Train or adapt a model meddeid-training[train] meddeid-data to organize training and test data
Score predictions or test stability meddeid-eval meddeid-eval[plots] for figures
Add support for another language meddeid-core Implement a separate meddeid-language-* package and model bundle

Common paths

text or a batch of notes → meddeid → redacted text + detected identifiers

Continue to local inference.

source notes → meddeid-data → meddeid-annotate
  → optional meddeid-curate → reviewed primary annotations

Continue to prepare and annotate data.

reviewed development data → meddeid-training → adapted bundle
  → meddeid batch → meddeid-eval

Continue to domain adaptation.

What you do not need

  • You do not need the grouped suite workspace. Python users install released packages directly from PyPI; browser-application users pull only the GHCR image they need.
  • You do not need training, evaluation, or annotation packages for ordinary inference.
  • You do not need curation for a completed single-reviewer dataset.
  • You do not need detailed character-level benchmark labels for ordinary model training.