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Family

The eds.family pipeline uses a simple rule-based algorithm to detect spans that describe a family member (or family history) of the patient rather than the patient themself.

Usage

The following snippet matches a simple terminology, and checks the family context of the extracted entities. It is complete, and can be run as is.

import spacy

nlp = spacy.blank("fr")
nlp.add_pipe("eds.sentences")
# Dummy matcher
nlp.add_pipe(
    "eds.matcher",
    config=dict(terms=dict(douleur="douleur", ostheoporose="osthéoporose")),
)
nlp.add_pipe("eds.family")

text = (
    "Le patient est admis le 23 août 2021 pour une douleur au bras. "
    "Il a des antécédents familiaux d'osthéoporose"
)

doc = nlp(text)

doc.ents
# Out: [douleur, osthéoporose]

doc.ents[0]._.family
# Out: False

doc.ents[1]._.family
# Out: True

Configuration

The pipeline can be configured using the following parameters :

Parameter Explanation Default
attr spaCy attribute to match on (eg NORM, TEXT, LOWER) "NORM"
family Family patterns None (use pre-defined patterns)
termination Termination patterns (for syntagma/proposition extraction) None (use pre-defined patterns)
use_sections Whether to use pre-annotated sections (requires the sections pipeline) False
on_ents_only Whether to qualify pre-extracted entities only True
explain Whether to keep track of the cues for each entity False

Declared extensions

The eds.family pipeline declares two spaCy extensions, on both Span and Token objects :

  1. The family attribute is a boolean, set to True if the pipeline predicts that the span/token relates to a family member.
  2. The family_ property is a human-readable string, computed from the family attribute. It implements a simple getter function that outputs PATIENT or FAMILY, depending on the value of family.

Authors and citation

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

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