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Welcome to the drug_named_entity_recognition wiki!
Named Entity Recognition is the task of recognising proper names and words from a special class in a document, such as product names, locations, people, or diseases. This can be compared to the related task of Named Entity Linking, where the products are linked to a unique ID.
In fact, this library performs both named entity recognition and named entity linking!
This is a lightweight Python library for finding drug names in a string. It doesn't use machine learning and isn't dependent on a particular external framework such as spaCy.
Please note this library finds only high confidence drugs.
It also only finds the English names of these drugs. Names in other languages are not supported.
It also doesn't find short code names of drugs, such as abbreviations commonly used in medicine, such as "Ceph" for "Cephradin" - as these are highly ambiguous.
Python 3.9 and above
pip install drug-named-entity-recognition
You must first tokenise your input text using a tokeniser of your choice (NLTK, spaCy, etc).
You pass a list of strings to the find_drugs function.
Example 1
from drug_named_entity_recognition import find_drugs
find_drugs("i bought some Phenoxymethylpenicillin".split(" "))
outputs a list of tuples.
[({'name': 'Phenoxymethylpenicillin',
'synonyms': {'Penicillin', 'Phenoxymethylpenicillin'},
'nhs_url': 'https://www.nhs.uk/medicines/phenoxymethylpenicillin',
'drugbank_id': 'DB00417'},
3,
3)]
You can ignore case with:
find_drugs("i bought some phenoxymethylpenicillin".split(" "), is_ignore_case=True)
The main data source is from Drugbank, augmented by datasets from the NHS, MeSH, Medline Plus and Wikipedia.
If you want to update the dictionary, you can use the data dump from Drugbank and replace the file drugbank vocabulary.csv:
- Download the open data dump from https://go.drugbank.com/releases/latest#open-data
If you want to update the Wikipedia dictionary, download the dump from:
and run extract_drug_names_and_synonyms_from_wikipedia_dump.py
If you want to update the dictionary, download the open data dump from https://www.nlm.nih.gov/
and run extract_drug_names_and_synonyms_from_mesh_dump.py