Disorders

Presentation

The following components extract various mentions of disorders. At the moment, the available components match the 16 different conditions from the Charlson Comorbidity Index. Each component is based on the ContextualMatcher component. Some general considerations about those components:

• Extracted entities are stored in the doc.spans dictionary. For instance, the eds.tobacco component stores matches in doc.spans["tobacco"].
• The comorbidity is also available under the ent.label_ of each match.
• Matches have an associated _.status attribute taking the value 0, 1, or 2. A corresponding _.detailled_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",
    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,
        ),
    ),
)

• 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 developped and trained specificaly 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.

On the medical definition of the comorbidities

Those components were developped to extract chronic and symptomatic conditions only.

Aggregation

For relevant phenotyping, matches should be aggregated at the document-level. For instance, a document might mention a complicated diabetes at the beginning ("Le patient a une rétinopathie diabétique"), and then refer to this diabetes without mentionning that it is complicated anymore ("Concernant son diabète, le patient ..."). Thus, a good and simple aggregation rule is, for each comorbidity, to

• disregard all entities tagged as irrelevant by the qualification component(s)
• take the maximum (i.e., the most severe) status of the leftover entities

An implementation of this rule is presented here

Usage

import spacy

nlp = spacy.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("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 = doc.spans["tobacco"]
tobacco[0]._.detailled_status
# Out: "ABSTINENCE"

tobacco[0]._.assigned["PA"]  # paquet-année
# Out: 10 # 


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