Drugs

The eds.drugs pipeline component detects mentions of French drugs (brand names and active ingredients) and adds them to doc.ents. Each drug is mapped to an ATC code through the Romedi terminology (Cossin et al., 2019). The ATC classifies drugs into groups.

Examples

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

import edsnlp, edsnlp.pipes as eds

nlp = edsnlp.blank("eds")
nlp.add_pipe(eds.normalizer())
nlp.add_pipe(eds.drugs(term_matcher="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[0]
# Out: ('Kardégic', 'B01AC06')

len(drugs_detected)
# Out: 5

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.

Parameters

PARAMETER	DESCRIPTION
`nlp`	The pipeline object TYPE: `PipelineProtocol`
`name`	The name of the component TYPE: `str` DEFAULT: `'drugs'`
`attr`	The default attribute to use for matching. TYPE: `str` DEFAULT: `'NORM'`
`ignore_excluded`	Whether to skip excluded tokens (requires an upstream pipeline to mark excluded tokens). TYPE: `bool` DEFAULT: `False`
`ignore_space_tokens`	Whether to skip space tokens during matching. TYPE: `bool` DEFAULT: `False`
`term_matcher`	The matcher to use for matching phrases ? One of (exact, simstring) TYPE: `Literal['exact', 'simstring']` DEFAULT: `'exact'`
`term_matcher_config`	Parameters of the matcher term matcher TYPE: `Dict[str, Any]` DEFAULT: `{}`
`label`	Label name to use for the `Span` object and the extension TYPE: `str` DEFAULT: `'drug'`
`span_setter`	How to set matches on the doc TYPE: `SpanSetterArg` DEFAULT: `{'ents': True, 'drug': True}`

RETURNS	DESCRIPTION
`TerminologyMatcher`

Authors and citation

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

Cossin S., Lebrun L., Lobre G., Loustau R., Jouhet V., Griffier R., Mougin F., Diallo G. and Thiessard F., 2019. Romedi: An Open Data Source About French Drugs on the Semantic Web. {Studies in Health Technology and Informatics}. 264, pp.79-82. 10.3233/SHTI190187