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Sections

Detected sections are :

  • allergies
  • antécédents
  • antécédents familiaux
  • traitements entrée
  • conclusion
  • conclusion entrée
  • habitus
  • correspondants
  • diagnostic
  • données biométriques entrée
  • examens
  • examens complémentaires
  • facteurs de risques
  • histoire de la maladie
  • actes
  • motif
  • prescriptions
  • traitements sortie
  • evolution
  • modalites sortie
  • vaccinations
  • introduction

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The pipeline extracts section title. A "section" is then defined as the span of text between two titles.

Remarks : - section introduction corresponds to the span of text between the header "COMPTE RENDU D'HOSPITALISATION" (usually denoting the beginning of the document) and the title of the following detected section - this pipeline works well for hospitalization summaries (CRH), but not necessarily for all types of documents (in particular for emergency or scan summaries CR-IMAGERIE)

Use at your own risks

Should you rely on eds.sections for critical downstream tasks, make sure to validate the pipeline to make sure that the component works. For instance, the eds.history pipeline can use sections to make its predictions, but that possibility is deactivated by default.

Usage

The following snippet detects section titles. It is complete and can be run as is.

import spacy

nlp = spacy.blank("fr")
nlp.add_pipe("eds.normalizer")
nlp.add_pipe("eds.sections")

text = "CRU du 10/09/2021\n" "Motif :\n" "Patient admis pour suspicion de COVID"

doc = nlp(text)

doc.spans["section_titles"]
# Out: [Motif]

Configuration

The pipeline can be configured using the following parameters :

Parameter Explanation Default
sections Sections patterns None (use pre-defined patterns)
add_patterns Whether add endlines patterns True
attr spaCy attribute to match on, eg NORM or TEXT "NORM"
ignore_excluded Whether to ignore excluded tokens True

Declared extensions

The eds.sections pipeline adds two fields to the doc.spans attribute :

  1. The section_titles key contains the list of all section titles extracted using the list declared in the terms.py module.
  2. The sections key contains a list of sections, ie spans of text between two section titles (or the last title and the end of the document).

If the document has entities before calling this pipeline an attribute section is added to each entity.

Authors and citation

The eds.sections pipeline was developed by AP-HP's Data Science team.