medcat 1.0.0.dev39

Concept annotation tool for Electronic Health Records

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+)

1. Install MedCAT

pip install --upgrade medcat

2. Get the scispacy models:

pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.3.0/en_core_sci_lg-0.3.0.tar.gz

3. Downlad the Vocabulary and CDB from the Models section bellow

4. Quickstart:

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

# Load the vocab model you downloaded
vocab = Vocab.load(vocab_path)
# Load the cdb model you downloaded
cdb = CDB.load('<path to the cdb file>') 

# Create cat - each cdb comes with a config that was used
#to train it. You can change that config in any way you want, before or after creating cat.
cat = CAT(cdb=cdb, cdb.config, 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}
}