| --- |
| 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-conclusion-annotated |
|
|
| <div align="center"> |
| <p> |
| <a href="https://huggingface.co/spaces/HealthDataHub/PARTAGES" style="display:inline;"><img src="img/PARTAGES BASELINE_RVB.png" alt="Logo" style="height:20px;vertical-align:middle; margin-right:8px; display:inline; margin-bottom:0.2em; margin-top:0.2em;" title="PARTAGES project"; /></a> |
| <a href="https://www.etalab.gouv.fr/wp-content/uploads/2018/11/open-licence.pdf" style="display:inline;" title="Etalab 2.0 license"><img src="img/Logo-licence-ouverte2.svg" style="height:20px; display:inline; margin-bottom:0.2em; margin-top:0.2em;"/></a> |
| <a href="https://creativecommons.org/licenses/by/4.0/deed.en" style="display:inline;"><img src="https://mirrors.creativecommons.org/presskit/buttons/88x31/png/by.png" style="height:20px; display:inline; margin-bottom:0.2em; margin-top:0.2em;" title="CC BY 4.0 license"/></a> |
| </p> |
| </div> |
|
|
| ## Reporting Issues & Contributing |
|
|
| If you encounter any errors or inconsistencies in this dataset, please report them in the discussion section of the "Community" tab on Hugging Face. |
|
|
| For more substantial contributions or collaboration opportunities, feel free to contact us directly. |
|
|
| ## Dataset Description |
|
|
| - **Points of Contact:** NÉVÉOL Aurélie, ZANELLA Laura, ZWEIGENBAUM Pierre |
|
|
| ## Dataset Summary |
|
|
| PARHAF-conclusion-annotated is a subpart of the PARHAF corpus, an open French corpus of human-authored clinical reports of fictional patients. |
|
|
| It was designed for developing and evaluating NLP systems that generate and extract conclusion sections from French clinical reports. Each document is annotated with a conclusion span (character offsets into the full report text), identifying the conclusion section to be extracted. |
|
|
| This dataset contains **training data only**. The test corpora, that will be oficially used for the conclusion generation task, are currently under development. 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.** |
|
|
| A subset of 108 documents (~3 %) from the training corpus was annotated manually and used to evaluate a rule-based automatic conclusion extraction pipeline. Teams are free to use the full training and evaluation sets or define a different train/dev split configuration. For more details about that use case, please contact us. |
|
|
|
|
| Each patient record was: |
|
|
| - written by a senior medical resident |
| - reviewed by another senior medical resident, from the same specialty |
| - automatically annotated by a rule-based system |
| - the evaluation subset was manually curated by an annotator |
|
|
|
|
| ### Data Statistics |
|
|
| ## DATASET SUMMARY |
|
|
| | Indicator | Value | |
| | --- | --- | |
| | Complete Dataset | | |
| | Number of JSON files | 3772 | |
| | Total annotations | 7760 | |
| | Average document length | 3866.83 characters | |
|
|
| ### Complete dataset distribution by type |
|
|
| | Type | Annotations | % of total | |
| | --- | --- | --- | |
| | DocumentMetadata | 3880 | 50.00% | |
| | Span | 3880 | 50.00% | |
|
|
| ### Complete dataset distribution by populated field |
|
|
| | Type.Field | Occurrences | % of total annot. | |
| | --- | --- | --- | |
| | DocumentMetadata.Nomenclature | 3880 | 50.00% | |
| | Span.Conclusion | 3880 | 50.00% | |
|
|
| ### Train / Dev Split Results |
|
|
| | Indicator | TRAIN | DEV | |
| | --- | --- | --- | |
| | Number of files | 3664 | 108 | |
| | Number of annotations | 3664 | 108 | |
| | Percentage of dataset | 97,13% | 2,86% | |
| | Avg. document length (chars) | 3867 | 3854 | |
|
|
|
|
| ### Train / Dev distribution by type |
|
|
| | Type | TRAIN | DEV | % train of type | |
| | --- | --- | --- | --- | |
| | DocumentMetadata | 3664 | 108 | 97,13% | |
| | Span | 3664 | 108 | 2.86% | |
|
|
|
|
| ### Train / Dev distribution by type |
|
|
| | Type.Field | TRAIN | DEV | % train of field | |
| | --- | --- | --- | --- | |
| DocumentMetadata.Nomenclature | 3664 | 108 | 97,13% | |
| | Span.Conclusion | 3664 | 108 | 2.86% | |
|
|
| ### Train / Dev distribution by value |
|
|
| | Type.Field.Value | TRAIN | DEV | |
| | --- | --- | --- | |
| DocumentMetadata.Nomenclature.ConclusionSpan | 3664 | 108 | 97,13% | |
| | Span.Conclusion.Conclusion | 3664 | 108 | 2.86% | |
|
|
|
|
|
|
| ### Data Origin |
|
|
| The clinical reports are extracted from the PARHAF corpus. Please refer to PARHAF documentation for more information about this corpus. |
|
|
| The documents were selected to represent a diversity of report types and specialties, following the distribution of reports with conclusions in the PARHAF 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. |
|
|
| 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. |
|
|
| ```python |
| import pandas as pd |
| from datasets import load_dataset |
| |
| dfs = {cfg: load_dataset( |
| "HealthDataHub/PARHAF-conclusion-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 |
| annot_type = patient_raw.annotation_type |
| nomenclature = patient_raw.attribute_Nomenclature |
| report_spans = dfs["spans"][dfs["spans"]["report"] == report_id] |
| ... |
| ``` |
|
|
| ### Data Fields |
|
|
| | Path | Type | Description | Possible values | |
| |------|------|-------------|-----------------| |
| | **document_metadata** | | | | |
| | `report` | string | Unique document identifier | — | |
| | `full_text` | string | Full text of the medical report | — | |
| | `annotation_type` | string | Annotation layer name | `"DocumentMetadata"` | |
| | `attribute_Nomenclature` | string | Document-level annotation label | `"ConclusionSpan"` | |
| | **spans** | | | | |
| | `report` | string | Document identifier | — | |
| | `span_id` | integer | Annotation identifier | — | |
| | `span_type` | string | Annotation type name | `"Span"` | |
| | `begin` | integer | Start character offset in `full_text` | — | |
| | `end` | integer | End character offset in `full_text` | — | |
| | `span_text` | string | Text of the annotated conclusion | — | |
| | `attribute_Conclusion` | string | Annotation feature value | `"Conclusion"` | |
|
|
| ### 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: guidelines/guidelines.md |
|
|
| ### Licensing Information |
|
|
| - CC BY 4.0 |
| - Etalab 2.0 |
|
|
| ### Citation Information |
|
|
| [More Information Needed] |
