Drugs

The eds.drugs pipeline component detects mentions of French drugs (brand names and active ingredients) and adds them to doc.ents. Each drug has an associated ATC code. ATC classifies drugs into groups.

Usage

In this example, we are looking for an oral antidiabetic medication (ATC code: A10B).

from edsnlp.pipelines.core.terminology import TerminologyTermMatcher
import spacy

nlp = spacy.blank("fr")
nlp.add_pipe("eds.normalizer")
nlp.add_pipe("eds.drugs", config=dict(term_matcher=TerminologyTermMatcher.exact))

text = "Traitement habituel: Kardégic, cardensiel (bisoprolol), glucophage, lasilix"

doc = nlp(text)

drugs_detected = [(x.text, x.kb_id_) for x in doc.ents]

drugs_detected
# Out: [('Kardégic', 'B01AC06'), ('cardensiel', 'C07AB07'), ('bisoprolol', 'C07AB07'), ('glucophage', 'A10BA02'), ('lasilix', 'C03CA01')]

oral_antidiabetics_detected = list(
    filter(lambda x: (x[1].startswith("A10B")), drugs_detected)
)
oral_antidiabetics_detected
# Out: [('glucophage', 'A10BA02')]

Glucophage is the brand name of a medication that contains metformine, the first-line medication for the treatment of type 2 diabetes.

Configuration

The pipeline can be configured using the following parameters :

Parameter	Description	Default
term_matcher	Which algorithm should we use : exact or simstring	"LOWER"
term_matcher_config	Config of the algorithm (SimstringMatcher's for simstring)	"LOWER"
attr	spaCy attribute to match on (eg NORM, TEXT, LOWER)	"NORM"
ignore_excluded	Whether to ignore excluded tokens for matching	False

Authors and citation

The eds.drugs pipeline was developed by the IAM team and CHU de Bordeaux's Data Science team.