Myocardial infarction[source]
The eds.myocardial_infarction
pipeline component extracts mentions of myocardial infarction. It will notably match:
- Mentions of various diseases (see below)
- Mentions of stents with a heart localization
Details of the used patterns
# fmt: off
from ..terms import HEART
main_pattern = dict(
source="main",
regex=[
r"coronaropathie",
r"angor.{1,5}instable",
r"cardiopathie(?!.{0,20}non).{0,20}(ischem|arteriosc)",
r"cardio.?myopathie(?!.{0,20}non).{0,20}(ischem|arteriosc)",
r"ischemi.{1,15}myocard",
r"syndrome.{1,5}corona.{1,10}aigu",
r"syndrome.{1,5}corona.{1,10}st",
r"pontage.{1,5}mammaire",
],
regex_attr="NORM",
)
with_localization = dict(
source="with_localization",
regex=[
r"\bstent",
r"endoprothese",
r"pontage",
r"anevr[iy]sme",
"infarctus",
r"angioplasti",
],
assign=[
dict(
name="heart_localized",
regex="(" + r"|".join(HEART) + ")",
window=(-10, 10),
),
],
regex_attr="NORM",
)
acronym = dict(
source="acronym",
regex=[
r"\bidm\b",
r"\bsca\b",
r"\batl\b",
],
regex_attr="NORM",
assign=dict(
name="segment",
regex=r"st([+-])",
window=2,
),
)
default_patterns = [
main_pattern,
with_localization,
acronym,
]
# fmt: on
Extensions
On each span span
that match, the following attributes are available:
span._.detailed_status
: set to Nonespan._.assigned
: dictionary with the following keys, if relevant:heart_localized
: localization of the stent or bypass
Examples
import edsnlp, edsnlp.pipes as eds
nlp = edsnlp.blank("eds")
nlp.add_pipe(eds.sentences())
nlp.add_pipe(
eds.normalizer(
accents=True,
lowercase=True,
quotes=True,
spaces=True,
pollution=dict(
information=True,
bars=True,
biology=True,
doctors=True,
web=True,
coding=True,
footer=True,
),
),
)
nlp.add_pipe(eds.myocardial_infarction())
Below are a few examples:
text = "Une cardiopathie ischémique"
doc = nlp(text)
spans = doc.spans["myocardial_infarction"]
spans
# Out: [cardiopathie ischémique]
text = "Une cardiopathie non-ischémique"
doc = nlp(text)
spans = doc.spans["myocardial_infarction"]
spans
# Out: []
text = "Présence d'un stent sur la marginale"
doc = nlp(text)
spans = doc.spans["myocardial_infarction"]
spans
# Out: [stent sur la marginale]
span = spans[0]
span._.assigned
# Out: {'heart_localized': [marginale]}
text = "Présence d'un stent périphérique"
doc = nlp(text)
spans = doc.spans["myocardial_infarction"]
spans
# Out: []
text = "infarctus du myocarde"
doc = nlp(text)
spans = doc.spans["myocardial_infarction"]
spans
# Out: [infarctus du myocarde]
span = spans[0]
span._.assigned
# Out: {'heart_localized': [myocarde]}
Parameters
PARAMETER | DESCRIPTION |
---|---|
nlp | The pipeline TYPE: |
name | The name of the component TYPE: |
patterns | The patterns to use for matching TYPE: |
label | The label to use for the TYPE: |
span_setter | How to set matches on the doc TYPE: |
Authors and citation
The eds.myocardial_infarction
component was developed by AP-HP's Data Science team with a team of medical experts, following the insights of the algorithm proposed by Petit-Jean et al., 2024.
Petit-Jean T., Gérardin C., Berthelot E., Chatellier G., Frank M., Tannier X., Kempf E. and Bey R., 2024. Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions. Journal of the American Medical Informatics Association. 31, pp.1280-1290. 10.1093/jamia/ocae069