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Alcohol consumption

The eds.alcohol pipeline component extracts mentions of alcohol consumption. It won't match occasional consumption, nor acute intoxication.

Details of the used patterns
# fmt: off
default_patterns = dict(
    source="alcohol",
    regex=[
        r"\balco[ol]",
        r"\bethyl",
        r"(?<!(25.?)|(sevrage)).?\boh\b",
        r"exogenose",
        r"delirium.tremens",
    ],
    exclude=[
        dict(
            regex=[
                "occasion",
                "episod",
                "festi",
                "rare",
                "libre",  # OH-libres
                "aigu",
            ],
            window=(-3, 5),
        ),
        dict(
            regex=["pansement", "compress"],
            window=-3,
        ),
    ],
    regex_attr="NORM",
    assign=[
        dict(
            name="stopped",
            regex=r"(?<!non )(?<!pas )(sevr|arret|stop|ancien)",
            window=(-3, 5),
        ),
        dict(
            name="zero_after",
            regex=r"(?=^[a-z]*\s*:?[\s-]*(0|oui|non(?! sevr)))",
            window=6,
        ),
    ],
)
# 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 alcohol dependence

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.alcohol")

Below are a few examples:

text = "Patient alcoolique."
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: [alcoolique]
text = "OH chronique."
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: [OH]
text = "Prise d'alcool occasionnelle"
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: []
text = "Application d'un pansement alcoolisé"
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: []
text = "Alcoolisme sevré"
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: [Alcoolisme sevré]

span = spans[0]

span._.detailed_status
# Out: ABSTINENCE

span._.assigned
# Out: {'stopped': [sevré]}
text = "Alcoolisme non sevré"
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: [Alcoolisme]
text = "Alcool: 0"
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: [Alcool: 0]

span = spans[0]

span._.detailed_status
# Out: ABSENT

span._.assigned
# Out: {'zero_after': [0]}
text = "Le patient est en cours de sevrage éthylotabagique"
doc = nlp(text)
spans = doc.spans["alcohol"]

spans
# Out: [sevrage éthylotabagique]

span = spans[0]

span._.detailed_status
# Out: ABSTINENCE

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

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

TYPE: Union[Dict[str, Any], List[Dict[str, Any]]] DEFAULT: {'source': 'alcohol', 'regex': ['\\balco[ol]', ...

label

The label to use for the Span object and the extension

TYPE: str DEFAULT: alcohol

span_setter

How to set matches on the doc

TYPE: SpanSetterArg DEFAULT: {'ents': True, 'alcohol': True}

Authors and citation

The eds.alcohol 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.