Behaviors

Presentation

EDS-NLP offers two components to extract behavioral patterns, namely the tobacco and alcohol consumption status. Each component is based on the ContextualMatcher component. Some general considerations about those components:

Extracted entities are stored in doc.ents and doc.spans. For instance, the eds.tobacco component stores matches in doc.spans["tobacco"].
The matched comorbidity is also available under the ent.label_ of each match.
Matches have an associated _.status attribute taking the value 1, or 2. A corresponding _.detailed_status attribute stores the human-readable status, which can be component-dependent. See each component documentation for more details.
Some components add additional information to matches. For instance, the tobacco adds, if relevant, extracted pack-year (= paquet-année). Those information are available under the ent._.assigned attribute.

Those components work on normalized documents. Please use the eds.normalizer pipeline with the following parameters:

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,
        ),
    ),
)

Use qualifiers

Those components should be used with a qualification pipeline to avoid extracted unwanted matches. At the very least, you can use available rule-based qualifiers (eds.negation, eds.hypothesis and eds.family). Better, a machine learning qualification component was developed and trained specifically for those components. For privacy reason, the model isn't publicly available yet.

Use the ML model

The model will soon be available in the models catalogue of AP-HP's CDW.

Usage

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.tobacco())
nlp.add_pipe(eds.diabetes())

text = """
Compte-rendu de consultation.

Je vois ce jour M. SCOTT pour le suivi de sa rétinopathie diabétique.
Le patient va bien depuis la dernière fois.
Je le félicite pour la poursuite de son sevrage tabagique (toujours à 10 paquet-année).

Sur le plan de son diabète, la glycémie est stable.
"""

doc = nlp(text)

doc.spans
# Out: {
# 'pollutions': [],
# 'tobacco': [sevrage tabagique (toujours à 10 paquet-année],
# 'diabetes': [rétinopathie diabétique, diabète]
# }

tobacco_matches = doc.spans["tobacco"]
tobacco_matches[0]._.detailed_status
# Out: "ABSTINENCE" #

tobacco_matches[0]._.assigned["PA"]  # paquet-année
# Out: 10 # (1)


diabetes = doc.spans["diabetes"]
(diabetes[0]._.detailed_status, diabetes[1]._.detailed_status)
# Out: ('WITH_COMPLICATION', 'WITHOUT_COMPLICATION') # (2)

Here we see an example of additional information that can be extracted
Here we see the importance of document-level aggregation to extract the correct severity of each comorbidity.