---
annotations_creators:
- expert-generated
language:
- es
tags:
- biomedical
- clinical
- spanish
multilinguality:
- monolingual
task_categories:
- token-classification
task_ids:
- named-entity-recognition
license:
- cc-by-4.0
---
# CANTEMIST
## Dataset Description
Manually classified collection of Spanish oncological clinical case reports.
- **Homepage:** [zenodo](https://zenodo.org/record/3978041)
- **Paper:** [Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results](https://www.researchgate.net/profile/Antonio-Miranda-Escalada-2/publication/352786464_Named_Entity_Recognition_Concept_Normalization_and_Clinical_Coding_Overview_of_the_Cantemist_Track_for_Cancer_Text_Mining_in_Spanish_Corpus_Guidelines_Methods_and_Results/links/60d98a3b458515d6fbe382d8/Named-Entity-Recognition-Concept-Normalization-and-Clinical-Coding-Overview-of-the-Cantemist-Track-for-Cancer-Text-Mining-in-Spanish-Corpus-Guidelines-Methods-and-Results.pdf)
- **Point of Contact:** encargo-pln-life@bsc.es
### Dataset Summary
Collection of 1301 oncological clinical case reports written in Spanish, with tumor morphology mentions manually annotated and mapped by clinical experts to a controlled terminology. Every tumor morphology mention is linked to an eCIE-O code (the Spanish equivalent of ICD-O).
The training subset contains 501 documents, the development subsets 500, and the test subset 300. The original dataset is distributed in [Brat](https://brat.nlplab.org/standoff.html) format.
This dataset was designed for the CANcer TExt Mining Shared Task, sponsored by [Plan-TL](https://plantl.mineco.gob.es/Paginas/index.aspx).
For further information, please visit [the official website](https://temu.bsc.es/cantemist/).
### Supported Tasks
Named Entity Recognition (NER)
### Languages
- Spanish (es)
### Directory Structure
* README.md
* cantemist.py
* train.conll
* dev.conll
* test.conll
## Dataset Structure
### Data Instances
Three four-column files, one for each split.
### Data Fields
Every file has 4 columns:
* 1st column: Word form or punctuation symbol
* 2nd column: Original BRAT file name
* 3rd column: Spans
* 4th column: IOB tag
#### Example
El cc_onco101 662_664 O
informe cc_onco101 665_672 O
HP cc_onco101 673_675 O
es cc_onco101 676_678 O
compatible cc_onco101 679_689 O
con cc_onco101 690_693 O
adenocarcinoma cc_onco101 694_708 B-MORFOLOGIA_NEOPLASIA
moderadamente cc_onco101 709_722 I-MORFOLOGIA_NEOPLASIA
diferenciado cc_onco101 723_735 I-MORFOLOGIA_NEOPLASIA
que cc_onco101 736_739 O
afecta cc_onco101 740_746 O
a cc_onco101 747_748 O
grasa cc_onco101 749_754 O
peripancreática cc_onco101 755_770 O
sobrepasando cc_onco101 771_783 O
la cc_onco101 784_786 O
serosa cc_onco101 787_793 O
, cc_onco101 793_794 O
infiltración cc_onco101 795_807 O
perineural cc_onco101 808_818 O
. cc_onco101 818_819 O
### Data Splits
| Split | Size |
| ------------- | ------------- |
| `train` | 19,397 |
| `dev` | 18,165 |
| `test` | 11,168 |
## Dataset Creation
### Curation Rationale
For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines.
### Source Data
#### Initial Data Collection and Normalization
The selected clinical case reports are fairly similar to hospital health records. To increase the usefulness and practical relevance of the CANTEMIST corpus, we selected clinical cases affecting all genders and that comprised most ages (from children to the elderly) and of various complexity levels (solid tumors, hemato-oncological malignancies, neuroendocrine cancer...).
The CANTEMIST cases include clinical signs and symptoms, personal and family history, current illness, physical examination, complementary tests (blood tests, imaging, pathology), diagnosis, treatment (including adverse effects of chemotherapy), evolution and outcome.
#### Who are the source language producers?
Humans, there is no machine generated data.
### Annotations
#### Annotation process
The manual annotation of the Cantemist corpus was performed by clinical experts following the Cantemist guidelines (for more detail refer to this [paper](http://ceur-ws.org/Vol-2664/cantemist_overview.pdf)). These guidelines contain rules for annotating morphology neoplasms in Spanish oncology clinical cases, as well as for mapping these annotations to eCIE-O.
A medical doctor was regularly consulted by annotators (scientists with PhDs on cancer-related subjects) for the most difficult pathology expressions. This same doctor periodically checked a random selection of annotated clinical records and these annotations were compared and discussed with the annotators. To normalize a selection of very complex cases, MD specialists in pathology from one of the largest university hospitals in Spain were consulted.
#### Who are the annotators?
Clinical experts.
### Personal and Sensitive Information
No personal or sensitive information included.
## Considerations for Using the Data
### Social Impact of Dataset
This corpus contributes to the development of medical language models in Spanish.
### Discussion of Biases
Not applicable.
## Additional Information
### Dataset Curators
The Text Mining Unit from Barcelona Supercomputing Center.
### Citation Information
```bibtex
@article{cantemist,
title={Named Entity Recognition, Concept Normalization and Clinical Coding: Overview of the Cantemist Track for Cancer Text Mining in Spanish, Corpus, Guidelines, Methods and Results.},
author={Miranda-Escalada, Antonio and Farr{\'e}, Eul{\`a}lia and Krallinger, Martin},
journal={IberLEF@ SEPLN},
pages={303--323},
year={2020}
}
```
### Contact Information
For further information, send an email to encargo-pln-life@bsc.es.
### Funding
This work was funded by the Spanish State Secretariat for Digitalization and Artificial Intelligence (SEDIA) within the framework of the Plan-TL.
### Licensing Information
This work is licensed under a Attribution 4.0 International License.