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Medical History

The eds.history pipeline uses a simple rule-based algorithm to detect spans that describe medical history rather than the diagnostic of a given visit.

The mere definition of an medical history is not straightforward. Hence, this component only tags entities that are explicitly described as part of the medical history, eg preceded by a synonym of "medical history".

This component may also use the output of the eds.sections pipeline. In that case, the entire antécédent section is tagged as a medical history.

Warning

Be careful, the eds.sections component may oversize the antécédents section. Indeed, it detects section titles and tags the entire text between a title and the next as a section. Hence, should a section title goes undetected after the antécédents title, some parts of the document will erroneously be tagged as a medical history.

To curb that possibility, using the output of the eds.sections component is deactivated by default.

Usage

The following snippet matches a simple terminology, and checks whether the extracted entities are history or not. 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", malaise="malaises")),
)
nlp.add_pipe("eds.history")

text = (
    "Le patient est admis le 23 août 2021 pour une douleur au bras. "
    "Il a des antécédents de malaises."
)

doc = nlp(text)

doc.ents
# Out: [douleur, malaises]

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

doc.ents[1]._.history
# 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"
history History 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.history pipeline declares two spaCy extensions, on both Span and Token objects :

  1. The history attribute is a boolean, set to True if the pipeline predicts that the span/token is a medical history.
  2. The history_ property is a human-readable string, computed from the history attribute. It implements a simple getter function that outputs CURRENT or ATCD, depending on the value of history.

Authors and citation

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

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