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edsnlp.pipelines.misc.consultation_dates

consultation_dates

ConsultationDates

Bases: GenericMatcher

Class to extract consultation dates from "CR-CONS" documents.

The pipeline populates the doc.spans['consultation_dates'] list.

For each extraction s in this list, the corresponding date is available as s._.consultation_date.

PARAMETER DESCRIPTION
nlp

Language pipeline object

TYPE: Language

consultation_mention

List of RegEx for consultation mentions.

  • If type==list: Overrides the default list
  • If type==bool: Uses the default list of True, disable if False

TYPE: Union[List[str], bool]

town_mention : Union[List[str], bool] List of RegEx for all AP-HP hospitals' towns mentions.

- If `type==list`: Overrides the default list
- If `type==bool`: Uses the default list of True, disable if False

document_date_mention : Union[List[str], bool] List of RegEx for document date.

- If `type==list`: Overrides the default list
- If `type==bool`: Uses the default list of True, disable if False
Source code in edsnlp/pipelines/misc/consultation_dates/consultation_dates.py 
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class ConsultationDates(GenericMatcher):
    """
    Class to extract consultation dates from "CR-CONS" documents.

    The pipeline populates the `#!python doc.spans['consultation_dates']` list.

    For each extraction `s` in this list, the corresponding date is available
    as `s._.consultation_date`.

    Parameters
    ----------
    nlp : Language
        Language pipeline object
    consultation_mention : Union[List[str], bool]
        List of RegEx for consultation mentions.

        - If `type==list`: Overrides the default list
        - If `type==bool`: Uses the default list of True, disable if False

    town_mention : Union[List[str], bool]
        List of RegEx for all AP-HP hospitals' towns mentions.

        - If `type==list`: Overrides the default list
        - If `type==bool`: Uses the default list of True, disable if False
    document_date_mention : Union[List[str], bool]
        List of RegEx for document date.

        - If `type==list`: Overrides the default list
        - If `type==bool`: Uses the default list of True, disable if False
    """

    def __init__(
        self,
        nlp: Language,
        consultation_mention: Union[List[str], bool],
        town_mention: Union[List[str], bool],
        document_date_mention: Union[List[str], bool],
        attr: str,
        **kwargs,
    ):

        logger.warning("This pipeline is still in beta")
        logger.warning(
            "This pipeline should ONLY be used on notes "
            "where `note_class_source_value == 'CR-CONS'`"
        )
        logger.warning(
            """This pipeline requires to use the normalizer pipeline with:
        lowercase=True,
        accents=True,
        quotes=True"""
        )

        if not (nlp.has_pipe("dates") and nlp.get_pipe("dates").on_ents_only is False):

            config = dict(**DEFAULT_CONFIG)
            config["on_ents_only"] = "consultation_mentions"

            self.date_matcher = Dates(nlp, **config)

        else:
            self.date_matcher = None

        if not consultation_mention:
            consultation_mention = []
        elif consultation_mention is True:
            consultation_mention = consult_regex.consultation_mention

        if not document_date_mention:
            document_date_mention = []
        elif document_date_mention is True:
            document_date_mention = consult_regex.document_date_mention

        if not town_mention:
            town_mention = []
        elif town_mention is True:
            town_mention = consult_regex.town_mention

        regex = dict(
            consultation_mention=consultation_mention,
            town_mention=town_mention,
            document_date_mention=document_date_mention,
        )

        super().__init__(
            nlp,
            regex=regex,
            terms=dict(),
            attr=attr,
            ignore_excluded=False,
            **kwargs,
        )

        self.set_extensions()

    @staticmethod
    def set_extensions() -> None:
        if not Span.has_extension("consultation_date"):
            Span.set_extension("consultation_date", default=None)

    def __call__(self, doc: Doc) -> Doc:
        """
        Finds entities

        Parameters
        ----------
        doc: spaCy Doc object

        Returns
        -------
        doc: Doc
            spaCy Doc object with additional
            `doc.spans['consultation_dates]` `SpanGroup`
        """

        ents = self.process(doc)

        doc.spans["consultation_mentions"] = ents
        doc.spans["consultation_dates"] = []

        if self.date_matcher is not None:
            doc = self.date_matcher(doc)

        for mention in ents:
            # Looking for a date
            # - In the same sentence
            # - Not less than 10 tokens AFTER the consultation mention
            matching_dates = [
                date
                for date in doc.spans["dates"]
                if (
                    (mention.sent == date.sent)
                    and (date.start > mention.start)
                    and (date.start - mention.end <= 10)
                )
            ]

            if matching_dates:
                # We keep the first mention of a date
                kept_date = min(matching_dates, key=lambda d: d.start)
                span = doc[mention.start : kept_date.end]
                span.label_ = mention.label_
                span._.consultation_date = kept_date._.date

                doc.spans["consultation_dates"].append(span)

        del doc.spans["consultation_mentions"]

        return doc
date_matcher = Dates(nlp, None=config) instance-attribute
__init__(nlp, consultation_mention, town_mention, document_date_mention, attr, **kwargs)
Source code in edsnlp/pipelines/misc/consultation_dates/consultation_dates.py 
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def __init__(
    self,
    nlp: Language,
    consultation_mention: Union[List[str], bool],
    town_mention: Union[List[str], bool],
    document_date_mention: Union[List[str], bool],
    attr: str,
    **kwargs,
):

    logger.warning("This pipeline is still in beta")
    logger.warning(
        "This pipeline should ONLY be used on notes "
        "where `note_class_source_value == 'CR-CONS'`"
    )
    logger.warning(
        """This pipeline requires to use the normalizer pipeline with:
    lowercase=True,
    accents=True,
    quotes=True"""
    )

    if not (nlp.has_pipe("dates") and nlp.get_pipe("dates").on_ents_only is False):

        config = dict(**DEFAULT_CONFIG)
        config["on_ents_only"] = "consultation_mentions"

        self.date_matcher = Dates(nlp, **config)

    else:
        self.date_matcher = None

    if not consultation_mention:
        consultation_mention = []
    elif consultation_mention is True:
        consultation_mention = consult_regex.consultation_mention

    if not document_date_mention:
        document_date_mention = []
    elif document_date_mention is True:
        document_date_mention = consult_regex.document_date_mention

    if not town_mention:
        town_mention = []
    elif town_mention is True:
        town_mention = consult_regex.town_mention

    regex = dict(
        consultation_mention=consultation_mention,
        town_mention=town_mention,
        document_date_mention=document_date_mention,
    )

    super().__init__(
        nlp,
        regex=regex,
        terms=dict(),
        attr=attr,
        ignore_excluded=False,
        **kwargs,
    )

    self.set_extensions()
set_extensions()
Source code in edsnlp/pipelines/misc/consultation_dates/consultation_dates.py 
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@staticmethod
def set_extensions() -> None:
    if not Span.has_extension("consultation_date"):
        Span.set_extension("consultation_date", default=None)
__call__(doc)

Finds entities

PARAMETER DESCRIPTION
doc

TYPE: Doc

RETURNS DESCRIPTION
doc

spaCy Doc object with additional doc.spans['consultation_dates] SpanGroup
Source code in edsnlp/pipelines/misc/consultation_dates/consultation_dates.py 
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def __call__(self, doc: Doc) -> Doc:
    """
    Finds entities

    Parameters
    ----------
    doc: spaCy Doc object

    Returns
    -------
    doc: Doc
        spaCy Doc object with additional
        `doc.spans['consultation_dates]` `SpanGroup`
    """

    ents = self.process(doc)

    doc.spans["consultation_mentions"] = ents
    doc.spans["consultation_dates"] = []

    if self.date_matcher is not None:
        doc = self.date_matcher(doc)

    for mention in ents:
        # Looking for a date
        # - In the same sentence
        # - Not less than 10 tokens AFTER the consultation mention
        matching_dates = [
            date
            for date in doc.spans["dates"]
            if (
                (mention.sent == date.sent)
                and (date.start > mention.start)
                and (date.start - mention.end <= 10)
            )
        ]

        if matching_dates:
            # We keep the first mention of a date
            kept_date = min(matching_dates, key=lambda d: d.start)
            span = doc[mention.start : kept_date.end]
            span.label_ = mention.label_
            span._.consultation_date = kept_date._.date

            doc.spans["consultation_dates"].append(span)

    del doc.spans["consultation_mentions"]

    return doc

factory

DEFAULT_CONFIG = dict(consultation_mention=True, town_mention=False, document_date_mention=False, attr='NORM') module-attribute

create_component(nlp, name, attr, consultation_mention, town_mention, document_date_mention)

Source code in edsnlp/pipelines/misc/consultation_dates/factory.py 
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@deprecated_factory(
    "consultation_dates",
    "eds.consultation_dates",
    default_config=DEFAULT_CONFIG,
)
@Language.factory("eds.consultation_dates", default_config=DEFAULT_CONFIG)
def create_component(
    nlp: Language,
    name: str,
    attr: str,
    consultation_mention: Union[List[str], bool],
    town_mention: Union[List[str], bool],
    document_date_mention: Union[List[str], bool],
):
    return ConsultationDates(
        nlp,
        attr=attr,
        consultation_mention=consultation_mention,
        document_date_mention=document_date_mention,
        town_mention=town_mention,
    )

patterns

consultation_mention = ['rendez-vous pris', 'consultation', 'consultation.{1,8}examen', 'examen clinique', 'de compte rendu', "date de l'examen", 'examen realise le', 'date de la visite'] module-attribute

town_mention = ['paris', 'kremlin.bicetre', 'creteil', 'boulogne.billancourt', 'villejuif', 'clamart', 'bobigny', 'clichy', 'ivry.sur.seine', 'issy.les.moulineaux', 'draveil', 'limeil', 'champcueil', 'roche.guyon', 'bondy', 'colombes', 'hendaye', 'herck.sur.mer', 'labruyere', 'garches', 'sevran', 'hyeres'] module-attribute

document_date_mention = ['imprime le', 'signe electroniquement', 'signe le', 'saisi le', 'dicte le', 'tape le', 'date de reference', 'date\\s*:', 'dactylographie le', 'date du rapport'] module-attribute

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