edsnlp.pipes.ner.frailty.psychological_status.factory
create_component = registry.factory.register('eds.psychological_status', assigns=['doc.ents', 'doc.spans'])(PsychologicalStatusMatcher) module-attribute [source]
The eds.psychological_status pipeline component extracts mentions of the psychological status.
Details of the used patterns
# fmt: off
from ..utils import (
ALTERED_STATUS_COMPLEMENTS,
HEALTHY_STATUS_COMPLEMENTS,
make_assign_regex,
make_include_dict_from_list,
make_status_assign,
normalize_space_characters,
)
healthy = dict(
source="healthy",
regex=[
"etat thymique correct",
"a le moral",
"euthymique",
],
regex_attr="NORM",
)
severe = dict(
source="severe",
regex=[
"perte de l'elan vital",
r"idees? suicidaires?",
r"idees? noires?",
],
regex_attr="NORM",
)
altered = dict(
source="altered",
regex=[
r"angoissee?",
"anhedonie",
r"anti[\s-]?depresseur",
r"anxieu(se|x)",
"anxiete",
"axiolytique",
"depreciation",
"depression",
r"deprimee?",
r"(?:anxio[-\s]?)?depressi(?:f|ve)",
"souffrance psychique",
r"syndrome (?:anxio[-\s]?)?depressif",
"thymie basse",
"tristesse",
"triste",
r"moral (?:moyen|bas)",
r"anxiolytique",
r"anxiolyse",
r"dysthymi(?:e|que)",
"desesperee?",
"desespoir",
"perte d'envie",
],
regex_attr="NORM",
)
sleep = dict(
source="other_sleep",
regex=["sommeil", "endormissement", r"\bdort\b"],
regex_attr="NORM",
exclude=[
dict(name="apnee", regex=["apnee"], window=-4),
dict(
name="medecine",
regex=["gelule", "cachet", "comprime", "souhaite"],
window=(-6, 6),
),
dict(
name="paradoxal",
regex=["paradoxal"],
window=3,
),
],
assign=[
dict(
name="sleep_healthy",
regex=make_assign_regex(HEALTHY_STATUS_COMPLEMENTS),
window=(-4, 4),
),
dict(
name="sleep_bad",
regex=make_assign_regex(ALTERED_STATUS_COMPLEMENTS),
window=(-4, 4),
),
],
)
insomnia = dict(
source="altered_insomnia",
regex=["insomnie"],
regex_attr="NORM",
assign=[
dict(
name="thymic_insomnia",
regex=make_assign_regex([r"reveil\sprecoce", "endormissement"]),
window=4,
)
],
)
night = dict(
source="other_night",
regex=["nuits?"],
regex_attr="NORM",
assign=make_status_assign(-2, 2, priority=False),
include=make_include_dict_from_list(make_status_assign(-2, 2)),
)
troubles = dict(
source="other_troubles",
regex=[r"(?<!bilan de )(?<!bilan )troubles?", "anomalies?"],
regex_attr="NORM",
assign=dict(
name="trouble_complement",
regex=make_assign_regex(["sommeil"]),
window=8,
required=True,
),
exclude=dict(
name="paradoxal",
regex=["paradoxal"],
window=9,
),
)
frailty = dict(
source="altered_frailty",
regex=["fragilite", "fragile"],
regex_attr="NORM",
assign=dict(
name="frailty_complement",
regex=make_assign_regex(["thymique"]),
window=6,
required=True,
),
)
ralentissement = dict(
source="other_ralentissement",
regex=["ralentissement"],
assign=dict(
name="complement",
regex=make_assign_regex(["ideatoire"]),
window=3,
required=True,
),
)
morale = dict(
source="other_morale",
regex=[r"\bmoral\b"],
regex_attr="NORM",
assign=make_status_assign(-4, 4),
include=make_include_dict_from_list(make_status_assign(-4, 4)),
)
other = dict(
source="other",
regex=[
"avis psychiatrique",
"antecedent psy",
"thymie",
"(?:sur le )?plan thymique",
"examen psychiatrique",
"persecution",
r"nuits? difficiles?",
"delire",
"delirant",
"insomnie",
"somnolence",
"psychiatre",
"psychologue",
"tendance a l'endormissement",
r"\bapathi(?:e|que)",
r"hallucinations?",
"clinophilie",
],
regex_attr="NORM",
)
status = dict(
source="other_status",
regex=[
"statut thymique",
"etat thymique",
r"etat (?:neuro[\s-]?)?psychologique",
r"statut (?:neuro[\s-]?)?psychologique",
],
assign=make_status_assign(),
)
default_patterns = normalize_space_characters(
[
healthy,
altered,
severe,
other,
ralentissement,
sleep,
insomnia,
troubles,
morale,
night,
status,
frailty,
]
)
# fmt: on
Extensions
On each span span that match, the following attribute is available:
span._.psychological_status: set to None.
It will specify the severity of the mention regarding the psychological status of the patient.
Possible values are:
healthy: this span suggests the patient is well regarding that domain.altered_nondescript: this span suggests the patient is not well, but it is not yet possible to ascertain the degree of alteration.altered_mild: this span suggests a light alteration regarding this domain.altered_severe: this span suggests a severe alteration regarding this domain.other: this span is not indicative of the level of alteration regarding this domain. Still, it hints that this domain has been evaluated.
Examples
import edsnlp, edsnlp.pipes as eds
nlp = edsnlp.blank("eds")
nlp.add_pipe(eds.sentences())
nlp.add_pipe(eds.normalizer())
nlp.add_pipe(f"eds.psychological_status")
Below are a few examples:
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: |