Medical oncept Annotation Tool

MedCAT can be used to extract information from Electronic Health Records (EHRs) and link it to biomedical ontologies like SNOMED-CT and UMLS. Preprint arXiv.

Demo

A demo application is available at MedCAT. Please note that this was trained on MedMentions and contains a small portion of UMLS.

Tutorial

A guide on how to use MedCAT is available in the tutorial folder. Read more about MedCAT on Towards Data Science.

Papers that use MedCAT

Treatment with ACE-inhibitors is not associated with early severe SARS-Covid-19 infection in a multi-site UK acute Hospital Trust
Supplementing the National Early Warning Score (NEWS2) for anticipating early deterioration among patients with COVID-19 infection
Comparative Analysis of Text Classification Approaches in Electronic Health Records
Experimental Evaluation and Development of a Silver-Standard for the MIMIC-III Clinical Coding Dataset

Related Projects

MedCATtrainer - an interface for building, improving and customising a given Named Entity Recognition and Linking (NER+L) model (MedCAT) for biomedical domain text.
MedCATservice - implements the MedCAT NLP application as a service behind a REST API.
iCAT - A docker container for CogStack/MedCAT/HuggingFace development in isolated environments.

Install using PIP (Requires Python 3.6.1+)

Install MedCAT

pip install --upgrade medcat

Get the scispacy models:

pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.4/en_core_sci_md-0.2.4.tar.gz

Download the Vocabulary and CDB from the Models section below
Quickstart:

from medcat.cat import CAT
from medcat.utils.vocab import Vocab
from medcat.cdb import CDB 

vocab = Vocab()
# Load the vocab model you downloaded
vocab.load_dict('<path to the vocab file>')

# Load the cdb model you downloaded
cdb = CDB()
cdb.load_dict('<path to the cdb file>') 

# create cat
cat = CAT(cdb=cdb, vocab=vocab)

# Test it
text = "My simple document with kidney failure"
doc_spacy = cat(text)
# Print detected entities
print(doc_spacy.ents)

# Or to get an array of entities, this will return much more information
#and usually easier to use unless you know a lot about spaCy
doc = cat.get_entities(text)
print(doc)

Models

A basic trained model is made public for the vocabulary and CDB. It is trained for the ~ 35K concepts available in MedMentions. It is quite limited so the performance might not be the best.

Vocabulary Download - Built from MedMentions

CDB Download - Built from MedMentions

(Note: This is was compiled from MedMentions and does not have any data from NLM as that data is not publicaly available.)

SNOMED-CT and UMLS

If you have access to UMLS or SNOMED-CT and can provide some proof (a screenshot of the UMLS profile page is perfect, feel free to redact all information you do not want to share), contact us - we are happy to share the pre-built CDB and Vocab for those databases.

Acknowledgement

Entity extraction was trained on MedMentions In total it has ~ 35K entites from UMLS

The vocabulary was compiled from Wiktionary In total ~ 800K unique words

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A big thank you goes to spaCy and Hugging Face - who made life a million times easier.

Citation

@misc{kraljevic2019medcat,
    title={MedCAT -- Medical Concept Annotation Tool},
    author={Zeljko Kraljevic and Daniel Bean and Aurelie Mascio and Lukasz Roguski and Amos Folarin and Angus Roberts and Rebecca Bendayan and Richard Dobson},
    year={2019},
    eprint={1912.10166},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

researchase / MedCAT