Tobacco consumption

The eds.tobacco pipeline component extracts mentions of tobacco consumption.

Details of the used patterns

# fmt: off
PA = r"(?:\bp/?a\b|paquets?.?annee)"
QUANTITY = r"(?P<quantity>[\d]{1,3})"
PUNCT = r"\.,-;\(\)"

default_patterns = [
    dict(
        source="tobacco",
        regex=[
            r"tabagi",
            r"tabac",
            r"\bfume\b",
            r"\bfumeu",
            r"\bpipes?\b",
        ],
        exclude=dict(
            regex=[
                "occasion",
                "moder",
                "quelqu",
                "festi",
                "rare",
                "sujet",  # Example : Chez le sujet fumeur ... generic sentences
            ],
            window=(-3, 5),
        ),
        regex_attr="NORM",
        assign=[
            dict(
                name="stopped",
                regex=r"(?<!non )(?<!pas )(\bex\b|sevr|arret|stop|ancien)",
                window=(-3, 15),
            ),
            dict(
                name="zero_after",
                regex=r"(?=^[a-z]*\s*:?[\s-]*(0|non(?! sevr)))",
                window=6,
            ),
            dict(
                name="PA",
                regex=rf"{QUANTITY}[^{PUNCT}]{{0,10}}{PA}|{PA}[^{PUNCT}]{{0,10}}{QUANTITY}",
                window=(-10, 10),
                reduce_mode="keep_first",
            ),
            dict(
                name="secondhand",
                regex="(passif)",
                window=5,
                reduce_mode="keep_first",
            ),
        ],
    )
]
# fmt: on

Extensions

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

span._.detailed_status: set to either
- "PRESENT"
- "ABSTINENCE" if the patient stopped its consumption
- "ABSENT" if the patient has no tobacco dependence
span._.assigned: dictionary with the following keys, if relevant:
- PA: the mentioned year-pack (= paquet-année)
- secondhand: if secondhand smoking

Examples

import edsnlp

nlp = edsnlp.blank("eds")
nlp.add_pipe("eds.sentences")
nlp.add_pipe(
    "eds.normalizer",
    config=dict(
        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(f"eds.tobacco")

Below are a few examples:

1234567

text = "Tabagisme évalué à 15 PA"
doc = nlp(text)
spans = doc.spans["tobacco"]

spans
# Out: [Tabagisme évalué à 15 PA]

span = spans[0]

span._.assigned
# Out: {'PA': 15}

text = "Patient tabagique"
doc = nlp(text)
spans = doc.spans["tobacco"]

spans
# Out: [tabagique]

text = "Tabagisme festif"
doc = nlp(text)
spans = doc.spans["tobacco"]

spans
# Out: []

text = "On a un tabagisme ancien"
doc = nlp(text)
spans = doc.spans["tobacco"]

spans
# Out: [tabagisme ancien]

span = spans[0]

span._.detailed_status
# Out: ABSTINENCE

span._.assigned
# Out: {'stopped': [ancien]}

text = "Tabac: 0"
doc = nlp(text)
spans = doc.spans["tobacco"]

spans
# Out: [Tabac: 0]

span = spans[0]

span._.detailed_status
# Out: ABSENT

span._.assigned
# Out: {'zero_after': [0]}

text = "Tabagisme passif"
doc = nlp(text)
spans = doc.spans["tobacco"]

spans
# Out: [Tabagisme passif]

span = spans[0]

span._.detailed_status
# Out: ABSENT

span._.assigned
# Out: {'secondhand': passif}

text = "Tabac: sevré depuis 5 ans"
doc = nlp(text)
spans = doc.spans["tobacco"]

spans
# Out: [Tabac: sevré]

span = spans[0]

span._.detailed_status
# Out: ABSTINENCE

span._.assigned
# Out: {'stopped': [sevré]}

Parameters

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

Authors and citation

The eds.tobacco component was developed by AP-HP's Data Science team with a team of medical experts. A paper describing in details the development of those components is being drafted and will soon be available.