medcat 0.2.4.3

Concept annotation tool for Electronic Health Records

Medical oncept Annotation Tool

A simple tool for concept annotation from UMLS or any other source.

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%).

Install using PIP

1. Install MedCAT

pip install --upgrade medcat

2. Install the scispacy models

pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.0/en_core_sci_md-0.2.0.tar.gz

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

4. How to use:

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)
cat.train = False

# Test it
doc = "My simple document with kidney failure"
doc_spacy = cat(doc)
# Entities are in
doc_spacy._.ents
# Or to get a json
doc_json = cat.get_json(doc)

# To have a look at the results:
from spacy import displacy
# Note that this will not show all entites, but only the longest ones
displacy.serve(doc_spacy, style='ent')

# To train - unsupervised, set the train flag to True and run
#documents through MedCAT
cat.train = True

# To run cat on a large number of documents, this will
#also run trainnig as the flag is set to True.
data = [(<doc_id>, <text>), (<doc_id>, <text>), ...]
docs = cat.multi_processing(data)

# To explicitly run trainnig you can do
f = open("<some file with a lot of medical text>", 'r')
# If you want fine tune set it to True, old training will be preserved
cat.run_training(f, fine_tune=True)

Building a new Concept Database

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>')

# If you have an existing CDB
cdb = CDB()
cdb.load_dict('<path to the cdb file>') 

# You can now add concepts from a CSV file, examples of the files can be found in ./examples
preparator = PrepareCDB(vocab=vocab)
csv_paths = ['<path to your csv_file>', '<another one>', ...] 
# e.g.
csv_paths = ['./examples/simple_cdb.csv']
cdb = preparator.prepare_csvs(csv_paths)

# Save the new CDB for later
cdb.save_dict("<path to a file where it will be saved>")
# Done

If building from source, the requirements are

python >= 3.5

All the rest can be instaled using pip from the requirements.txt file, by running:

pip install -r requirements.txt

Results

Dataset	SoftF1	Description
MedMentions	0.84	The whole MedMentions dataset without any modifications or supervised training
MedMentions	0.828	MedMentions only for concepts that require disambiguation, or names that map to more CUIs
MedMentions	0.97	Medmentions filterd by TUI to only concepts that are a disease

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 dictionary was compiled from Wiktionary In total ~ 800K unique words For now NOT made publicaly available