Dementia[source]

The eds.dementia pipeline component extracts mentions of dementia.

Details of the used patterns

# fmt: off
main_pattern = dict(
    source="main",
    regex=[
        r"demence",
        r"dementiel",
        r"corps de le[vw]y",
        r"deficits?.chroniques?.cognitif",
        r"troubles?.mnesique?",
        r"troubles?.praxique",
        r"troubles?.attentionel",
        r"troubles?.degeneratif.{1,15}fonctions.{1,5}sup",
        r"maladies?.cerebrales?.degen",
        r"troubles?.neurocogn",
        r"deficits?.cognitif",
        r"(trouble|dysfonction).{1,20} cogniti",
        r"atteinte.{1,7}spheres?cogniti",
        r"syndrome.{1,10}(frontal|neuro.deg)",
        r"dysfonction.{1,25}cogni",
        r"(?<!specialisee )alzheimer",
        r"demence.{1,20}(\balz|\bpark)",
        r"binswanger",
        r"gehring",
        r"\bpick",
        r"de guam",
        r"[kc]reutzfeld.{1,5}ja[ck]ob",
        r"huntington",
        r"korsako[fv]",
        r"atrophie.{1,10}(cortico|hippocamp|cereb|lobe)",
    ],
)

acronym = dict(
    source="acronym",
    regex=[
        r"\bSLA\b",
        r"\bDFT\b",
        r"\bDFT",
        r"\bTNC\b",
    ],
    regex_attr="TEXT",
    exclude=dict(
        regex=r"\banti",  # anticorps
        window=-15,
        regex_attr="NORM",
    ),
)

charcot = dict(
    source="charcot",
    regex=[
        r"maladie.{1,10}charcot",
    ],
    exclude=dict(
        regex=[
            "pied de",
            "marie.?tooth",
        ],
        window=(-3, 3),
    ),
)


default_patterns = [
    main_pattern,
    acronym,
    charcot,
]
# fmt: on

Extensions

On each span span that match, the following attributes are available:

span._.detailed_status: set to None

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.dementia())

Below are a few examples:

1234

text = "D'importants déficits cognitifs"
doc = nlp(text)
spans = doc.spans["dementia"]

spans
# Out: [déficits cognitifs]

text = "Patient atteint de démence"
doc = nlp(text)
spans = doc.spans["dementia"]

spans
# Out: [démence]

text = "On retrouve des anti-SLA"
doc = nlp(text)
spans = doc.spans["dementia"]

spans
# Out: []

text = "Une maladie de Charcot"
doc = nlp(text)
spans = doc.spans["dementia"]

spans
# Out: [maladie de Charcot]

Parameters

PARAMETER	DESCRIPTION
`nlp`	The pipeline TYPE: `Optional[PipelineProtocol]`
`name`	The name of the component TYPE: `Optional[str]`
`patterns`	The patterns to use for matching DEFAULT: `[{'source': 'main', 'regex': ['demence', 'demen...`
`label`	The label to use for the `Span` object and the extension TYPE: `str` DEFAULT: `dementia`
`span_setter`	The span setter to use TYPE: `SpanSetterArg` DEFAULT: `{'ents': True, 'dementia': True}`

Authors and citation

The eds.dementia 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