Hemiplegia[source]

The eds.hemiplegia pipeline component extracts mentions of hemiplegia.

Details of the used patterns

# fmt: off
main_pattern = dict(
    source="main",
    regex=[
        r"hemiplegi",
        r"tetraplegi",
        r"quadriplegi",
        r"paraplegi",
        r"neuropathie.{1,25}motrice.{1,30}type [5V]",
        r"charcot.?marie.?tooth",
        r"locked.?in",
        r"syndrome.{1,5}(enfermement|verrouillage)|(desafferen)",
        r"paralysie.{1,10}hemicorps",
        r"paralysie.{1,10}jambe",
        r"paralysie.{1,10}membre",
        r"paralysie.{1,10}cote",
        r"paralysie.{1,5}cerebrale.{1,5}spastique",
    ],
    regex_attr="NORM",
)

acronym = dict(
    source="acronym",
    regex=[
        r"\bLIS\b",
        r"\bNMSH\b",
    ],
    regex_attr="TEXT",
)

default_patterns = [
    main_pattern,
    acronym,
]
# 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.hemiplegia())

Below are a few examples:

123

text = "Patient hémiplégique"
doc = nlp(text)
spans = doc.spans["hemiplegia"]

spans
# Out: [hémiplégique]

text = "Paralysie des membres inférieurs"
doc = nlp(text)
spans = doc.spans["hemiplegia"]

spans
# Out: [Paralysie des membres]

text = "Patient en LIS"
doc = nlp(text)
spans = doc.spans["hemiplegia"]

spans
# Out: [LIS]

Parameters

PARAMETER	DESCRIPTION
`nlp`	The pipeline TYPE: `Optional[PipelineProtocol]`
`name`	The name of the component TYPE: `Optional[str]` DEFAULT: `'hemiplegia'`
`patterns`	The patterns to use for matching TYPE: `Union[Dict[str, Any], List[Dict[str, Any]]]` DEFAULT: `[{'source': 'main', 'regex': ['hemiplegi', 'tet...`
`label`	The label to use for the `Span` object and the extension TYPE: `str` DEFAULT: `hemiplegia`
`span_setter`	How to set matches on the doc TYPE: `SpanSetterArg` DEFAULT: `{'ents': True, 'hemiplegia': True}`

Authors and citation

The eds.hemiplegia 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