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"description": "# Medical ![](\"https://github.com/w-is-h/cat/blob/master/media/cat-logo.png\")
oncept Annotation Tool\n\nA simple tool for concept annotation from UMLS or any other source.\n\n## Demo\nA demo application is available at [MedCAT](https://medcat.rosalind.kcl.ac.uk). Please note that this was trained on MedMentions\nand contains a very small portion of UMLS (<1%). \n\n\n### Install using PIP\n1. Install MedCAT \n\n`pip install --upgrade medcat`\n\n2. Install the scispacy models\n\n`pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.2.3/en_core_sci_md-0.2.3.tar.gz`\n\n3. Downlad the Vocabulary and CDB from the Models section bellow\n\n4. How to use:\n```python\nfrom medcat.cat import CAT\nfrom medcat.utils.vocab import Vocab\nfrom medcat.cdb import CDB \n\nvocab = Vocab()\n# Load the vocab model you downloaded\nvocab.load_dict('')\n\n# Load the cdb model you downloaded\ncdb = CDB()\ncdb.load_dict('') \n\n# create cat\ncat = CAT(cdb=cdb, vocab=vocab)\ncat.train = False\n\n# Test it\ndoc = \"My simple document with kidney failure\"\ndoc_spacy = cat(doc)\n# Entities are in\ndoc_spacy._.ents\n# Or to get a json\ndoc_json = cat.get_json(doc)\n\n# To have a look at the results:\nfrom spacy import displacy\n# Note that this will not show all entites, but only the longest ones\ndisplacy.serve(doc_spacy, style='ent')\n\n# To train - unsupervised, set the train flag to True and run\n#documents through MedCAT\ncat.train = True\n\n# To run cat on a large number of documents, this will\n#also run trainnig as the flag is set to True.\ndata = [(, ), (, ), ...]\ndocs = cat.multi_processing(data)\n\n# To explicitly run trainnig you can do\nf = open(\"\", 'r')\n# If you want fine tune set it to True, old training will be preserved\ncat.run_training(f, fine_tune=True)\n```\n\n\n### Building a new Concept Database\n\n```python\nfrom medcat.cat import CAT\nfrom medcat.utils.vocab import Vocab\nfrom medcat.cdb import CDB \n\nvocab = Vocab()\n# Load the vocab model you downloaded\nvocab.load_dict('')\n\n# If you have an existing CDB\ncdb = CDB()\ncdb.load_dict('') \n\n# You can now add concepts from a CSV file, examples of the files can be found in ./examples\npreparator = PrepareCDB(vocab=vocab)\ncsv_paths = ['', '', ...] \n# e.g.\ncsv_paths = ['./examples/simple_cdb.csv']\ncdb = preparator.prepare_csvs(csv_paths)\n\n# Save the new CDB for later\ncdb.save_dict(\"\")\n# Done\n```\n\n## If building from source, the requirements are\n`python >= 3.5`\n\nAll the rest can be instaled using `pip` from the requirements.txt file, by running:\n\n`pip install -r requirements.txt`\n\n\n## Results\n\n| Dataset | SoftF1 | Description |\n| --- | :---: | --- |\n| MedMentions | 0.84 | The whole MedMentions dataset without any modifications or supervised training |\n| MedMentions | 0.828 | MedMentions only for concepts that require disambiguation, or names that map to more CUIs |\n| MedMentions | 0.97 | Medmentions filterd by TUI to only concepts that are a disease |\n\n\n## Models\nA 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\nso the performance might not be the best.\n\nVocabulary [Download](https://s3-eu-west-1.amazonaws.com/zkcl/vocab.dat) - Built from MedMentions\n\nCDB [Download](https://s3-eu-west-1.amazonaws.com/zkcl/cdb-medmen.dat) - Built from MedMentions\n\n\n(Note: This is was compiled from MedMentions and does not have any data from [NLM](https://www.nlm.nih.gov/research/umls/) as\nthat data is not publicaly available.)\n\n\n## Acknowledgement\nEntity extraction was trained on [MedMentions](https://github.com/chanzuckerberg/MedMentions) In total it has ~ 35K entites from UMLS\n\nThe dictionary was compiled from [Wiktionary](https://en.wiktionary.org/wiki/Wiktionary:Main_Page) In total ~ 800K unique words `For now NOT made publicaly available`\n\n\n",
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