Cerebrovascular accident[source]
The eds.cerebrovascular_accident
pipeline component extracts mentions of cerebrovascular accident. It will notably match:
- Mentions of AVC/AIT
- Mentions of bleeding, hemorrhage, thrombus, ischemia, etc., localized in the brain
Details of the used patterns
# fmt: off
import re
from edsnlp.utils.resources import get_AVC_care_site
from ..terms import BRAIN, HEART, PERIPHERAL
AVC_CARE_SITES_REGEX = [
r"\b" + re.escape(cs.strip()) + r"\b" for cs in get_AVC_care_site(prefix=True)
] + [
r"h[oô]p",
r"\brcp",
r"service",
r"\bsau",
r"ap.?hp",
r"\burg",
r"finess",
r"\bsiret",
r"[àa] avc",
r"consult",
]
avc = dict(
source="avc",
regex=[
r"\bavc\b",
],
exclude=[
dict(
regex=AVC_CARE_SITES_REGEX,
window=(-5, 5),
regex_flags=re.S | re.I,
limit_to_sentence=False,
),
dict(
regex=r"\b[a-z]\.",
window=2,
limit_to_sentence=False,
),
],
regex_attr="NORM",
)
with_localization = dict(
source="with_localization",
regex=[
r"(hemorr?agie|hematome)",
r"angiopath",
r"angioplasti",
r"infarctus",
r"occlusion",
r"saignement",
r"embol",
r"vascularite",
r"\bhsd\b",
r"thrombos",
r"thrombol[^y]",
r"thrombophi",
r"thrombi[^n]",
r"thrombus",
r"thrombectomi",
r"phleb",
],
regex_attr="NORM",
exclude=[
dict(
regex=r"pulmo|poumon",
window=4,
),
],
assign=[
dict(
name="brain_localized",
regex="(" + r"|".join(BRAIN) + ")",
window=(-15, 15),
limit_to_sentence=False,
include_assigned=False,
),
],
)
general = dict(
source="general",
regex=[
r"accident.{1,5}vasculaires.{1,5}cereb",
r"accident.{1,5}vasculaire.{1,5}ischemi",
r"accident.{1,5}ischemi",
r"moya.?moya",
r"occlusion.{1,5}(artere|veine).{1,20}retine",
r"vasculopathies?.cerebrales?.ischemique",
r"maladies?.des.petites.arteres",
r"maladies?.des.petits.vaisseaux",
r"thrombolyse",
r"\bsusac\b",
],
regex_attr="NORM",
)
acronym = dict(
source="acronym",
regex=[
r"\bAIC\b",
r"\bOACR\b",
r"\bOVCR\b",
],
regex_attr="TEXT",
)
AIT = dict(
source="AIT",
regex=[
r"\bAIC\b",
r"\bOACR\b",
r"\bOVCR\b",
r"\bAIT\b",
],
regex_attr="TEXT",
)
ischemia = dict(
source="ischemia",
regex=[
r"ischemi",
],
exclude=[
dict(
regex=PERIPHERAL + HEART,
window=(-7, 7),
),
],
assign=[
dict(
name="brain",
regex="(" + r"|".join(BRAIN) + ")",
window=(-10, 15),
),
],
regex_attr="NORM",
)
default_patterns = [
avc,
with_localization,
general,
acronym,
AIT,
ischemia,
]
# fmt: on
Extensions
On each span span
that match, the following attributes are available:
span._.detailed_status
: set to None
Usage
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.cerebrovascular_accident())
Below are a few examples:
text = "Patient hospitalisé à AVC."
doc = nlp(text)
spans = doc.spans["cerebrovascular_accident"]
spans
# Out: []
text = "Hospitalisation pour un AVC."
doc = nlp(text)
spans = doc.spans["cerebrovascular_accident"]
spans
# Out: [AVC]
text = "Saignement intracranien"
doc = nlp(text)
spans = doc.spans["cerebrovascular_accident"]
spans
# Out: [Saignement]
span = spans[0]
span._.assigned
# Out: {'brain_localized': [intracranien]}
text = "Thrombose périphérique"
doc = nlp(text)
spans = doc.spans["cerebrovascular_accident"]
spans
# Out: []
text = "Thrombose sylvienne"
doc = nlp(text)
spans = doc.spans["cerebrovascular_accident"]
spans
# Out: [Thrombose]
span = spans[0]
span._.assigned
# Out: {'brain_localized': [sylvienne]}
text = "Infarctus cérébral"
doc = nlp(text)
spans = doc.spans["cerebrovascular_accident"]
spans
# Out: [Infarctus]
span = spans[0]
span._.assigned
# Out: {'brain_localized': [cérébral]}
text = "Soigné via un thrombolyse"
doc = nlp(text)
spans = doc.spans["cerebrovascular_accident"]
spans
# Out: [thrombolyse]
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.cerebrovascular_accident
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