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Disorders

Presentation

The following components extract 16 different conditions from the Charlson Comorbidity Index. Each component is based on the ContextualMatcher component.

The components were developed by AP-HP's Data Science team with a team of medical experts, following the insights of the algorithm proposed by Petit-Jean et al., 2024

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:

    import edsnlp, edsnlp.pipes as eds
...

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.

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

  1. Petit-Jean T., Gérardin C., Berthelot E., Chatellier G., Frank M., Tannier X., Kempf E. and Bey R., 2024. Collaborative and privacy-enhancing workflows on a clinical data warehouse: an example developing natural language processing pipelines to detect medical conditions. Journal of the American Medical Informatics Association. 31, pp.1280-1290. 10.1093/jamia/ocae069