medcat 0.3.3.8

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. Paper on arXiv.

Demo

A demo application is available at MedCAT. Please note that this was trained on MedMentions and contains a very small portion of UMLS (<1%).

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

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.

Install using PIP

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/v0.2.4/en_core_sci_md-0.2.4.tar.gz

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

4. 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 a json
import json
doc_json = json.loads(cat.get_json(text))
print(doc_json)

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.)

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

Citation

If you are using MedCAT please give your best to also cite it.

@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}
}