Consultation Dates

This pipeline consists of two main parts:

A matcher which finds mentions of consultation events (more details below)
A date parser (see the corresponding pipeline) that links a date to those events

Usage

Note

It is designed to work ONLY on consultation notes (CR-CONS), so please filter accordingly before proceeding.

import spacy


nlp = spacy.blank("fr")
nlp.add_pipe("eds.sentences")
nlp.add_pipe(
    "eds.normalizer",
    config=dict(
        lowercase=True,
        accents=True,
        quotes=True,
        pollution=False,
    ),
)
nlp.add_pipe("eds.consultation_dates")

text = "XXX \n" "Objet : Compte-Rendu de Consultation du 03/10/2018. \n" "XXX "

doc = nlp(text)

doc.spans["consultation_dates"]
# Out: [Consultation du 03/10/2018]

doc.spans["consultation_dates"][0]._.consultation_date.to_datetime()
# Out: DateTime(2018, 10, 3, 0, 0, 0, tzinfo=Timezone('Europe/Paris'))

Consultation events

Three main families of terms are available by default to extract those events.

The `consultation_mention` terms

This list contains terms directly referring to consultations, such as "Consultation du..." or "Compte rendu du...". This list is the only one activated by default since it is fairly precise an not error-prone.

The `town_mention` terms

This list contains the towns of each AP-HP's hospital. Its goal is to fetch dates mentioned as "Paris, le 13 décembre 2015". It has a high recall but poor precision, since those dates can often be dates of letter redaction instead of consultation dates.

The `document_date_mention` terms

This list contains expressions mentioning the date of creation/edition of a document, such as "Date du rapport: 13/12/2015" or "Signé le 13/12/2015". As for town_mention, it has a high recall but is prone to errors since document date and consultation date aren't necessary similar.

Note

By default, only the consultation_mention are used

Configuration

The pipeline can be configured using the following parameters :

Parameter	Explanation	Default
`consultation_mention`	Whether to use consultation patterns, or list of patterns	`True` (use pre-defined patterns)
`town_mention`	Whether to use town patterns, or list of patterns	`False`
`document_date_mention`	Whether to use document date patterns, or list of patterns	`False`
`attr`	spaCy attribute to match on, eg `NORM` or `TEXT`	`"NORM"`

Declared extensions

The eds.consultation_dates pipeline declares one spaCy extensions on the Span object: the consultation_date attribute, which is a Python datetime object.

Authors and citation

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