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edsnlp.pipelines.qualifiers.history

factory

DEFAULT_CONFIG = dict(attr='NORM', history=patterns.history, termination=termination, use_sections=False, explain=False, on_ents_only=True) module-attribute

create_component(nlp, name, history, termination, use_sections, attr, explain, on_ents_only)

Source code in edsnlp/pipelines/qualifiers/history/factory.py 
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@deprecated_factory("antecedents", "eds.history", default_config=DEFAULT_CONFIG)
@deprecated_factory("eds.antecedents", "eds.history", default_config=DEFAULT_CONFIG)
@deprecated_factory("history", "eds.history", default_config=DEFAULT_CONFIG)
@Language.factory("eds.history", default_config=DEFAULT_CONFIG)
def create_component(
    nlp: Language,
    name: str,
    history: Optional[List[str]],
    termination: Optional[List[str]],
    use_sections: bool,
    attr: str,
    explain: str,
    on_ents_only: bool,
):
    return History(
        nlp,
        attr=attr,
        history=history,
        termination=termination,
        use_sections=use_sections,
        explain=explain,
        on_ents_only=on_ents_only,
    )

patterns

history = ['antécédents', 'atcd', 'atcds', 'tacds', 'antécédent'] module-attribute

history

History

Bases: Qualifier

Implements an history detection algorithm.

The components looks for terms indicating history in the text.

PARAMETER DESCRIPTION
nlp

spaCy nlp pipeline to use for matching.

TYPE: Language

history

List of terms indicating medical history reference.

TYPE: Optional[List[str]]

termination

List of syntagme termination terms.

TYPE: Optional[List[str]]

use_sections

Whether to use section pipeline to detect medical history section.

TYPE: bool

attr

spaCy's attribute to use: a string with the value "TEXT" or "NORM", or a dict with the key 'term_attr' we can also add a key for each regex.

TYPE: str

on_ents_only

Whether to look for matches around detected entities only. Useful for faster inference in downstream tasks.

TYPE: bool

regex

A dictionnary of regex patterns.

TYPE: Optional[Dict[str, Union[List[str], str]]]

explain

Whether to keep track of cues for each entity.

TYPE: bool

Source code in edsnlp/pipelines/qualifiers/history/history.py 
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class History(Qualifier):
    """
    Implements an history detection algorithm.

    The components looks for terms indicating history in the text.

    Parameters
    ----------
    nlp : Language
        spaCy nlp pipeline to use for matching.
    history : Optional[List[str]]
        List of terms indicating medical history reference.
    termination : Optional[List[str]]
        List of syntagme termination terms.
    use_sections : bool
        Whether to use section pipeline to detect medical history section.
    attr : str
        spaCy's attribute to use:
        a string with the value "TEXT" or "NORM", or a dict with the key 'term_attr'
        we can also add a key for each regex.
    on_ents_only : bool
        Whether to look for matches around detected entities only.
        Useful for faster inference in downstream tasks.
    regex : Optional[Dict[str, Union[List[str], str]]]
        A dictionnary of regex patterns.
    explain : bool
        Whether to keep track of cues for each entity.
    """

    defaults = dict(
        history=history,
        termination=termination,
    )

    def __init__(
        self,
        nlp: Language,
        attr: str,
        history: Optional[List[str]],
        termination: Optional[List[str]],
        use_sections: bool,
        explain: bool,
        on_ents_only: bool,
    ):

        terms = self.get_defaults(
            history=history,
            termination=termination,
        )

        super().__init__(
            nlp=nlp,
            attr=attr,
            on_ents_only=on_ents_only,
            explain=explain,
            **terms,
        )

        self.set_extensions()

        self.sections = use_sections and (
            "eds.sections" in nlp.pipe_names or "sections" in nlp.pipe_names
        )
        if use_sections and not self.sections:
            logger.warning(
                "You have requested that the pipeline use annotations "
                "provided by the `section` pipeline, but it was not set. "
                "Skipping that step."
            )

    @staticmethod
    def set_extensions() -> None:

        if not Token.has_extension("history"):
            Token.set_extension("history", default=False)

        if not Token.has_extension("antecedents"):
            Token.set_extension(
                "antecedents",
                getter=deprecated_getter_factory("antecedents", "history"),
            )

        if not Token.has_extension("antecedent"):
            Token.set_extension(
                "antecedent",
                getter=deprecated_getter_factory("antecedent", "history"),
            )

        if not Token.has_extension("history_"):
            Token.set_extension(
                "history_",
                getter=lambda token: "ATCD" if token._.history else "CURRENT",
            )

        if not Token.has_extension("antecedents_"):
            Token.set_extension(
                "antecedents_",
                getter=deprecated_getter_factory("antecedents_", "history_"),
            )

        if not Token.has_extension("antecedent_"):
            Token.set_extension(
                "antecedent_",
                getter=deprecated_getter_factory("antecedent_", "history_"),
            )

        if not Span.has_extension("history"):
            Span.set_extension("history", default=False)

        if not Span.has_extension("antecedents"):
            Span.set_extension(
                "antecedents",
                getter=deprecated_getter_factory("antecedents", "history"),
            )

        if not Span.has_extension("antecedent"):
            Span.set_extension(
                "antecedent",
                getter=deprecated_getter_factory("antecedent", "history"),
            )

        if not Span.has_extension("history_"):
            Span.set_extension(
                "history_",
                getter=lambda span: "ATCD" if span._.history else "CURRENT",
            )

        if not Span.has_extension("antecedents_"):
            Span.set_extension(
                "antecedents_",
                getter=deprecated_getter_factory("antecedents_", "history_"),
            )

        if not Span.has_extension("antecedent_"):
            Span.set_extension(
                "antecedent_",
                getter=deprecated_getter_factory("antecedent_", "history_"),
            )

        if not Span.has_extension("history_cues"):
            Span.set_extension("history_cues", default=[])

        if not Span.has_extension("antecedents_cues"):
            Span.set_extension(
                "antecedents_cues",
                getter=deprecated_getter_factory("antecedents_cues", "history_cues"),
            )

        if not Span.has_extension("antecedent_cues"):
            Span.set_extension(
                "antecedent_cues",
                getter=deprecated_getter_factory("antecedent_cues", "history_cues"),
            )

    def process(self, doc: Doc) -> Doc:
        """
        Finds entities related to history.

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

        Returns
        -------
        doc:
            spaCy Doc object, annotated for history
        """

        matches = self.get_matches(doc)

        terminations = get_spans(matches, "termination")
        boundaries = self._boundaries(doc, terminations)

        # Removes duplicate matches and pseudo-expressions in one statement
        matches = filter_spans(matches, label_to_remove="pseudo")

        entities = list(doc.ents) + list(doc.spans.get("discarded", []))
        ents = None

        sections = []

        if self.sections:
            sections = [
                Span(doc, section.start, section.end, label="ATCD")
                for section in doc.spans["sections"]
                if section.label_ == "antécédents"
            ]

        for start, end in boundaries:
            ents, entities = consume_spans(
                entities,
                filter=lambda s: check_inclusion(s, start, end),
                second_chance=ents,
            )

            sub_matches, matches = consume_spans(
                matches, lambda s: start <= s.start < end
            )

            sub_sections, sections = consume_spans(sections, lambda s: doc[start] in s)

            if self.on_ents_only and not ents:
                continue

            cues = get_spans(sub_matches, "history")
            cues += sub_sections

            history = bool(cues)

            if not self.on_ents_only:
                for token in doc[start:end]:
                    token._.history = history

            for ent in ents:
                ent._.history = ent._.history or history

                if self.explain:
                    ent._.history_cues += cues

                if not self.on_ents_only and ent._.history:
                    for token in ent:
                        token._.history = True

        return doc
defaults = dict(history=history, termination=termination) class-attribute
sections = use_sections and 'eds.sections' in nlp.pipe_names or 'sections' in nlp.pipe_names instance-attribute
__init__(nlp, attr, history, termination, use_sections, explain, on_ents_only)
Source code in edsnlp/pipelines/qualifiers/history/history.py 
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def __init__(
    self,
    nlp: Language,
    attr: str,
    history: Optional[List[str]],
    termination: Optional[List[str]],
    use_sections: bool,
    explain: bool,
    on_ents_only: bool,
):

    terms = self.get_defaults(
        history=history,
        termination=termination,
    )

    super().__init__(
        nlp=nlp,
        attr=attr,
        on_ents_only=on_ents_only,
        explain=explain,
        **terms,
    )

    self.set_extensions()

    self.sections = use_sections and (
        "eds.sections" in nlp.pipe_names or "sections" in nlp.pipe_names
    )
    if use_sections and not self.sections:
        logger.warning(
            "You have requested that the pipeline use annotations "
            "provided by the `section` pipeline, but it was not set. "
            "Skipping that step."
        )
set_extensions()
Source code in edsnlp/pipelines/qualifiers/history/history.py 
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@staticmethod
def set_extensions() -> None:

    if not Token.has_extension("history"):
        Token.set_extension("history", default=False)

    if not Token.has_extension("antecedents"):
        Token.set_extension(
            "antecedents",
            getter=deprecated_getter_factory("antecedents", "history"),
        )

    if not Token.has_extension("antecedent"):
        Token.set_extension(
            "antecedent",
            getter=deprecated_getter_factory("antecedent", "history"),
        )

    if not Token.has_extension("history_"):
        Token.set_extension(
            "history_",
            getter=lambda token: "ATCD" if token._.history else "CURRENT",
        )

    if not Token.has_extension("antecedents_"):
        Token.set_extension(
            "antecedents_",
            getter=deprecated_getter_factory("antecedents_", "history_"),
        )

    if not Token.has_extension("antecedent_"):
        Token.set_extension(
            "antecedent_",
            getter=deprecated_getter_factory("antecedent_", "history_"),
        )

    if not Span.has_extension("history"):
        Span.set_extension("history", default=False)

    if not Span.has_extension("antecedents"):
        Span.set_extension(
            "antecedents",
            getter=deprecated_getter_factory("antecedents", "history"),
        )

    if not Span.has_extension("antecedent"):
        Span.set_extension(
            "antecedent",
            getter=deprecated_getter_factory("antecedent", "history"),
        )

    if not Span.has_extension("history_"):
        Span.set_extension(
            "history_",
            getter=lambda span: "ATCD" if span._.history else "CURRENT",
        )

    if not Span.has_extension("antecedents_"):
        Span.set_extension(
            "antecedents_",
            getter=deprecated_getter_factory("antecedents_", "history_"),
        )

    if not Span.has_extension("antecedent_"):
        Span.set_extension(
            "antecedent_",
            getter=deprecated_getter_factory("antecedent_", "history_"),
        )

    if not Span.has_extension("history_cues"):
        Span.set_extension("history_cues", default=[])

    if not Span.has_extension("antecedents_cues"):
        Span.set_extension(
            "antecedents_cues",
            getter=deprecated_getter_factory("antecedents_cues", "history_cues"),
        )

    if not Span.has_extension("antecedent_cues"):
        Span.set_extension(
            "antecedent_cues",
            getter=deprecated_getter_factory("antecedent_cues", "history_cues"),
        )
process(doc)

Finds entities related to history.

PARAMETER DESCRIPTION
doc

spaCy Doc object

TYPE: Doc

RETURNS DESCRIPTION
doc

spaCy Doc object, annotated for history
Source code in edsnlp/pipelines/qualifiers/history/history.py 
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def process(self, doc: Doc) -> Doc:
    """
    Finds entities related to history.

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

    Returns
    -------
    doc:
        spaCy Doc object, annotated for history
    """

    matches = self.get_matches(doc)

    terminations = get_spans(matches, "termination")
    boundaries = self._boundaries(doc, terminations)

    # Removes duplicate matches and pseudo-expressions in one statement
    matches = filter_spans(matches, label_to_remove="pseudo")

    entities = list(doc.ents) + list(doc.spans.get("discarded", []))
    ents = None

    sections = []

    if self.sections:
        sections = [
            Span(doc, section.start, section.end, label="ATCD")
            for section in doc.spans["sections"]
            if section.label_ == "antécédents"
        ]

    for start, end in boundaries:
        ents, entities = consume_spans(
            entities,
            filter=lambda s: check_inclusion(s, start, end),
            second_chance=ents,
        )

        sub_matches, matches = consume_spans(
            matches, lambda s: start <= s.start < end
        )

        sub_sections, sections = consume_spans(sections, lambda s: doc[start] in s)

        if self.on_ents_only and not ents:
            continue

        cues = get_spans(sub_matches, "history")
        cues += sub_sections

        history = bool(cues)

        if not self.on_ents_only:
            for token in doc[start:end]:
                token._.history = history

        for ent in ents:
            ent._.history = ent._.history or history

            if self.explain:
                ent._.history_cues += cues

            if not self.on_ents_only and ent._.history:
                for token in ent:
                    token._.history = True

    return doc
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