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PARHAF-pseudo-annotated

Tasks:
Token Classification
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Languages:
French
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Tags:
inception
uima
annotation
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metadata 
license: cc-by-4.0
language:
  - fr
tags:
  - inception
  - uima
  - annotation
task_categories:
  - token-classification
configs:
  - config_name: document_metadata
    data_files:
      - split: train
        path: data/document_metadata/document_metadata_train.parquet
      - split: dev
        path: data/document_metadata/document_metadata_dev.parquet
  - config_name: spans
    data_files:
      - split: train
        path: data/spans/spans_train.parquet
      - split: dev
        path: data/spans/spans_dev.parquet

Dataset Card for PARHAF-pseudo-annotated

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Dataset Description

  • Points of Contact: WAJSBURT Perceval, KHALIL Youness

Dataset Summary

PARHAF-pseudo-annotated is a subpart of the PARHAF corpus, an open French corpus of human-authored clinical reports of fictional patients.

It was created to support the development and evaluation of clinical NLP systems for the pseudonymization of identifying entities in clinical reports..

This dataset contains training data only. The test set will remain under embargo to enable future evaluations under controlled conditions, limiting the risk of LLM contamination through prior data exposure. Please contact us for access to the test data.

This training dataset is divided into a train split (80%) and a dev split (20%) to facilitate experimental design and reproducibility across teams. Teams are free to use the full training set or define a different split configuration.

Each patient record was:

  • written by a senior medical resident
  • reviewed by another senior medical resident, from the same specialty
  • annotated by a specialist of the use case
  • curated by another specialist of the use case

Data statistics

DATASET SUMMARY

Indicator Value
Complete Dataset
Number of JSON files 509
Total annotations 6976
Average document length 4021 characters
80% Threshold (annotations) 5580

Complete dataset distribution by type

Type Annotations % of total
EntiteAnonymisation 6976 100.00%

Complete dataset distribution by populated field

Type.Field Occurrences % of total annot.
EntiteAnonymisation.Category 6976 100.00%
EntiteAnonymisation.RoleLOC 87 1.25%
EntiteAnonymisation.RoleNUM 8 0.11%
EntiteAnonymisation.RolePER 2466 35.35%

Train / Dev Split Results

Indicator TRAIN DEV
Number of files 408 101
Number of annotations 5580 1396
Percentage of dataset 79.99% 20.01%
Avg. document length (chars) 3957 4280

Train / Dev distribution by type

Type TRAIN DEV % train of type
EntiteAnonymisation 5580 1396 79.99%

Train / Dev distribution by populated field

Type.Field TRAIN DEV % train of field
EntiteAnonymisation.Category 5580 1396 79.99%
EntiteAnonymisation.RoleLOC 66 21 75.86%
EntiteAnonymisation.RoleNUM 5 3 62.50%
EntiteAnonymisation.RolePER 1971 495 79.93%

Train / Dev distribution by value

Type.Field.Value TRAIN DEV % train
EntiteAnonymisation.Category.IDENTIFYING_DATE 2312 568 80.28%
EntiteAnonymisation.Category.LAST_NAME 1111 287 79.47%
EntiteAnonymisation.RolePER.Patient 978 257 79.19%
EntiteAnonymisation.RolePER.Carer 973 232 80.75%
EntiteAnonymisation.Category.FIRST_NAME 860 208 80.52%
EntiteAnonymisation.Category.FAMILY_STATUS 767 203 79.07%
EntiteAnonymisation.Category.PATIENT_SOCIAL_IDENTITY 424 99 81.07%
EntiteAnonymisation.Category.CITY 43 11 79.63%
EntiteAnonymisation.RoleLOC.Patient 33 13 71.74%
EntiteAnonymisation.Category.COUNTRY 19 9 67.86%
EntiteAnonymisation.RolePER.Other 20 6 76.92%
EntiteAnonymisation.RoleLOC.Hospital 18 4 81.82%
EntiteAnonymisation.Category.UNIDENTIFYING_DATE 19 0 100.00%
EntiteAnonymisation.RoleLOC.Other 15 4 78.95%
EntiteAnonymisation.Category.PATIENT_BIRTHDATE 12 5 70.59%
EntiteAnonymisation.Category.PHONE_NUMBER 5 3 62.50%
EntiteAnonymisation.Category.PATIENT_NATIONALITY 4 2 66.67%
EntiteAnonymisation.RoleNUM.Carer 4 1 80.00%
EntiteAnonymisation.Category.ADDRESS 3 0 100.00%
EntiteAnonymisation.RoleNUM.Hospital 1 1 50.00%
EntiteAnonymisation.Category.URL 1 1 50.00%
EntiteAnonymisation.RoleNUM.Other 0 1 0.00%

Data Origin

The clinical reports are extracted from the PARHAF corpus. Please refer to PARHAF documentation for more information about this corpus.

Languages

  • fr_FR

Dataset Structure

We distribute both a Hugging Face dataset and a standalone version of the corpus. The standalone dataset consists of a JSON file per patient report, in UIMA CAS JSON format. This format constitutes the canonical version of the corpus. The Hugging Face dataset (Parquet/Arrow) is a derived representation generated automatically from the JSON files.

Both formats therefore contain identical information and differ only in storage layout.

One dataset instance corresponds to one report.

Hugging Face dataset

This snippet shows how to extract and iterate over medical report information per patient using the datasets library.

 import pandas as pd
 from datasets import load_dataset
 

dfs = {cfg: load_dataset("HealthDataHub/PARHAF-pseudo-annotated", cfg, split="train").to_pandas()
       for cfg in ["document_metadata", "spans"]}

for patient_raw in dfs["document_metadata"].itertuples():
    report_id = patient_raw.report
    text = patient_raw.full_text
    report_spans = dfs["spans"][dfs["spans"]["report"] == report_id]
    ...

Data Fields

Path Type Description Possible values
document_metadata
report string Identifiant unique du rapport
full_text string Texte intégral du rapport
spans
report string Identifiant du rapport
span_id integer Identifiant de l'annotation
span_type string Type de l'entité annotée EntiteAnonymisation
begin integer Offset de début
end integer Offset de fin
span_text string Texte de l'entité
attribute_Categorie string Categorie ADDRESS, CITY, COUNTRY, FAMILY_STATUS, FIRST_NAME, IDENTIFYING_DATE, LAST_NAME, PATIENT_BIRTHDATE, PATIENT_NATIONALITY, PATIENT_SOCIAL_IDENTITY
attribute_RolePER string RolePER Carer, Other, Patient
attribute_RoleLOC string RoleLOC Hospital, Other, Patient
attribute_RoleNUM string RoleNUM Carer, Hospital

Data Splits

Only the training set is released here. The remaining portion of the corpus will be temporarily embargoed to enable future evaluations under controlled conditions, thereby limiting the risk of large language model contamination through prior exposure to the data. You can evaluate your system on the test set through the CodaBench platform.

Annotation Guidelines

You can find the detailed annotation protocol here: annotation_guidelines.pdf

Licensing Information

This dataset is released under licenses:

  • CC BY 4.0
  • Etalab 2.0

Citation Information

[More Information Needed]