medcat 2.6.0
Medical Concept Annotation Toolkit (v2)
Requires Python: >=3.10
Keywords: ML, NLP, NER+L

# Medical  <img src="https://github.com/CogStack/cogstack-nlp/blob/main/media/cat-logo.png?raw=true" width=45> oncept Annotation Tool (version 2)

MedCAT can be used to extract information from Electronic Health Records (EHRs) and link it to biomedical ontologies like SNOMED-CT, UMLS, or HPO (and potentially other ontologies).
Original paper for v1 on [arXiv](https://arxiv.org/abs/2010.01165). 

## Why MedCAT v2?

MedCAT v2 is a comprehensive refactor designed to improve modularity, flexibility, and maintainability. The core library is now lightweight, with optional extras (spaCy tokenization, MetaCAT, DeID, RelCAT) available as separate installable features—allowing you to install only what you need. This modular approach reduces dependencies, enables smaller installs, and provides better separation of concerns. Additionally, v2 reduces internal coupling with spaCy, allowing for alternative tokenizers and greater extensibility. The new architecture makes it easier to create custom components and addons, while improving code maintainability and preparing the foundation for future enhancements. For most users, single-threaded inference APIs remain unchanged, ensuring a smooth transition.

**There's a number of breaking changes in MedCAT v2 compared to v1.**
When moving from v1 to v2, please refer to the [migration guide](docs/migration_guide_v2.md).
Details on breaking are outlined [here](docs/breaking_changes.md).

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**Official Docs [here](https://cogstack-nlp.readthedocs.io/)**

**Discussion Forum [discourse](https://discourse.cogstack.org/)**

## Available Models

We have 2 public v2 models available:
1) SnomedCT UK Clinical edition 39.0 (Oct 2024) and UK Drug Extension 39.0 (July 2024) based model enriched with UMLS 2024AA; trained only on MIMIC-IV
2) SnomedCT UK Clinical edition 40.2 (June 2025) and UK Drug Extension 40.3 (July 2024) based model enriched with UMLS 2024AA; trained only on MIMIC-IV

There are also a number of MedCAT v1 models available that can automatically be converted if required.

To download any of these models, please [follow this link](https://uts.nlm.nih.gov/uts/login?service=https://medcat.sites.er.kcl.ac.uk/auth-callback) (or [this link for API key based download](https://medcat.sites.er.kcl.ac.uk/auth-callback-api)) and sign into your NIH profile / UMLS license. You will then be redirected to the MedCAT model download form. Please complete this form and you will be provided a download link.

While we encourage you use MedCAT v2 and the models in that native format, if you download an older version MedCAT v2 will be able to load it and covnert it to the format it knows. However, the loading process will be considerably longerin those cases.

If you wish you can also convert the v1 models into the v2 format (see [tutorial](../medcat-v2-tutorials/notebooks/introductory/migration/1._Migrate_v1_model_to_v2.ipynb)).

```python
from medcat.utils.legacy import legacy_converter
from medcat.storage.serialisers import AvailableSerialisers
old_model = '<path to old v1 model>'
new_model_dir = '<dir to place new model in>'
legacy_converter.do_conversion(old_model_path, new_model_dir, AvailableSerialisers.dill)
```
OR
```bash
model_path = "models/medcat1_model_pack.zip"
new_model_folder = "models"  # file in this folder
! python -m  medcat.utils.legacy.legacy_converter $model_path $new_model_folder --verbose
```

## News
- **New public 2024 and 2025** Snomed models were uploaded and made available 7. October 2025.
- **MedCAT 2.0.0**  was released 18. August 2025.
<!-- - **Paper** van Es, B., Reteig, L.C., Tan, S.C. et al. [Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods](https://doi.org/10.1186/s12859-022-05130-x). BMC Bioinformatics 24, 10 (2023).
- **New tool in the Cogstack ecosystem \[19. December 2022\]** [Foresight -- Deep Generative Modelling of Patient Timelines using Electronic Health Records](https://arxiv.org/abs/2212.08072)
- **New Paper using MedCAT \[21. October 2022\]**: [A New Public Corpus for Clinical Section Identification: MedSecId.](https://aclanthology.org/2022.coling-1.326.pdf)
- **Major Change to the Permissions of Use \[4. August 2022\]** MedCAT now uses the [Elastic License 2.0](https://github.com/CogStack/MedCAT/pull/271/commits/c9f4e86116ec751a97c618c97dadaa23e1feb6bc). For further information please click [here.](https://www.elastic.co/licensing/elastic-license)
- **New Downloader \[15. March 2022\]**: You can now [download](https://uts.nlm.nih.gov/uts/login?service=https://medcat.rosalind.kcl.ac.uk/auth-callback) the latest SNOMED-CT and UMLS model packs via UMLS user authentication.
- **New Feature and Tutorial \[7. December 2021\]**: [Exploring Electronic Health Records with MedCAT and Neo4j](https://towardsdatascience.com/exploring-electronic-health-records-with-medcat-and-neo4j-f376c03d8eef)
- **New Minor Release \[20. October 2021\]** Introducing model packs, new faster multiprocessing for large datasets (100M+ documents) and improved MetaCAT.
- **New Release \[1. August 2021\]**: Upgraded MedCAT to use spaCy v3, new scispaCy models have to be downloaded - all old CDBs (compatble with MedCAT v1) will work without any changes.
- **New Feature and Tutorial \[8. July 2021\]**: [Integrating 🤗 Transformers with MedCAT for biomedical NER+L](https://towardsdatascience.com/integrating-transformers-with-medcat-for-biomedical-ner-l-8869c76762a)
- **General \[1. April 2021\]**: MedCAT is upgraded to v1, unforunately this introduces breaking changes with older models (MedCAT v0.4),
  as well as potential problems with all code that used the MedCAT package. MedCAT v0.4 is available on the legacy
  branch and will still be supported until 1. July 2021
  (with respect to potential bug fixes), after it will still be available but not updated anymore.
- **Paper**: [What’s in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarization](https://www.aclweb.org/anthology/2021.naacl-main.382.pdf)
- ([more...](https://github.com/CogStack/cogstack-nlp/blob/main/medcat-v2/media/news.md)) -->

## Installation

MedCAT v2 has its first full release
```
pip install medcat
```
Do note that **this installs only the core MedCAT v2**.
**It does not necessary dependencies for `spacy`-based tokenizing or MetaCATs or DeID**.
However, all of those are supported as well.
You can install them as follows:
```
pip install "medcat[spacy]" # for spacy-based tokenizer
pip install "medcat[meta-cat]"  # for MetaCAT
pip install "medcat[deid]"  # for DeID models
pip install "medcat[spacy,meta-cat,deid,rel-cat,dict-ner]"  # for all of the above
```

### Installing plugins

MedCAT v2 supports **external plugins** that can provide new components (e.g. alternative NER models, addons, tokenizers) via Python entry points.

- **Curated plugins**: The `medcat.plugins.catalog` module ships with a curated plugin catalog that can be updated from a remote JSON file.
- **Installer**: The `medcat.plugins.installer.PluginInstallationManager` wraps a `pip`-based installer and knows how to resolve a compatible plugin version for your current MedCAT version.
- **CLI**: You can install curated plugins directly from the command line:

```bash
python -m medcat plugins install medcat-gliner
```

This will:

- look up `medcat-gliner` in the curated catalog,
- resolve a version compatible with your installed MedCAT,
- and install it using `pip`.

You can also:

- pass `--dry-run` to show what would be installed without making changes:

  ```bash
  python -m medcat plugins install --dry-run medcat-gliner
  ```

- override the version/ref explicitly (e.g. when testing a branch or tag):

  ```bash
  python -m medcat plugins install medcat-gliner --force-version main
  ```

If a plugin requires authentication (for example, private Git repositories), MedCAT will log a warning and the installer will surface pip’s error messages if credentials are missing or incorrect.

### Version / update checking

MedCAT now has the ability to check for newer versions of itself on PyPI (or a local mirror of it).
This is so users don't get left behind too far with older versions of our software.
This is configurable by evnironmental variables so that sys admins (e.g for JupyterHub) can specify the settings they wish.
Version checks are done once a week and the results are cached.

Below is a table of the environmental variables that govern the version checking and their defaults.

| Variable | Default | Description |
|-----------|----------|-------------|
| **`MEDCAT_DISABLE_VERSION_CHECK`** | *(unset)* | When set to `true`, `yes` or `disable`, disables the version update check entirely. Useful for CI environments, offline setups, or deployments where external network access is restricted. |
| **`MEDCAT_PYPI_URL`** | `https://pypi.org/pypi` | Base URL used to query package metadata. Can be changed to a PyPI mirror or internal repository that exposes the `/pypi/{pkg}/json` API. |
| **`MEDCAT_MINOR_UPDATE_THRESHOLD`** | `3` | Number of newer **minor** versions (e.g. `1.4.x`, `1.5.x`) that must exist before MedCAT emits a “newer version available” log message. |
| **`MEDCAT_PATCH_UPDATE_THRESHOLD`** | `3` | Number of newer **patch** versions (e.g. `1.3.1`, `1.3.2`, `1.3.3`) on the same minor line required before emitting an informational update message. |
| **`MEDCAT_VERSION_UPDATE_LOG_LEVEL`** | `INFO` | Logging level used when reporting available newer versions (minor/patch thresholds). Accepts any valid `logging` level string (`DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`). |
| **`MEDCAT_VERSION_UPDATE_YANKED_LOG_LEVEL`** | `WARNING` | Logging level used when reporting that the current version has been **yanked** on PyPI. Accepts the same values as above. |

## Demo

The MedCAT v2 demo web app is available [here](https://medcat.sites.er.kcl.ac.uk/).

## Key Concepts

- **Components**: The building blocks of MedCAT (NER, Entity Linking, preprocessing, etc.)
- **Addons**: Components that extend the core NER+EL pipeline with additional processing stages
- **Plugins**: External packages that provide new component implementations or other functionality via entry points

See [Architecture Documentation](docs/architecture.md) for detailed information.

## Tutorials
A guide on how to use MedCAT v2 is available at [MedCATv2 Tutorials](../medcat-v2-tutorials).
However, the tutorials are a bit of a work in progress at this point in time.


## Acknowledgements
Entity extraction was trained on [MedMentions](https://github.com/chanzuckerberg/MedMentions) In total it has ~ 35K entites from UMLS

The vocabulary was compiled from [Wiktionary](https://en.wiktionary.org/wiki/Wiktionary:Main_Page) In total ~ 800K unique words

## Powered By
A big thank you goes to [spaCy](https://spacy.io/) and [Hugging Face](https://huggingface.co/) - who made life a million times easier.


<!-- ## Citation
```
@ARTICLE{Kraljevic2021-ln,
  title="Multi-domain clinical natural language processing with {MedCAT}: The Medical Concept Annotation Toolkit",
  author="Kraljevic, Zeljko and Searle, Thomas and Shek, Anthony and Roguski, Lukasz and Noor, Kawsar and Bean, Daniel and Mascio, Aurelie and Zhu, Leilei and Folarin, Amos A and Roberts, Angus and Bendayan, Rebecca and Richardson, Mark P and Stewart, Robert and Shah, Anoop D and Wong, Wai Keong and Ibrahim, Zina and Teo, James T and Dobson, Richard J B",
  journal="Artif. Intell. Med.",
  volume=117,
  pages="102083",
  month=jul,
  year=2021,
  issn="0933-3657",
  doi="10.1016/j.artmed.2021.102083"
}
``` -->