CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures

CasiMedicos-Arg is, to the best of our knowledge, the first multilingual dataset for Medical Question Answering where correct and incorrect diagnoses for a clinical case are enriched with a natural language explanation written by doctors. The casimedicos-exp have been manually annotated with argument components (i.e., premise, claim) and argument relations (i.e., attack, support). Thus, Multilingual CasiMedicos-arg dataset consists of 558 clinical cases (English, Spanish, French, Italian) with explanations, where we annotated 5021 claims, 2313 premises, 2431 support relations, and 1106 attack relations.

Antidote CasiMedicos-Arg splits
train	434
validation	63
test	125

• 📖 Paper:CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures
• 💻 Github Repo (Data and Code): https://github.com/ixa-ehu/antidote-casimedicos
• 🌐 Project Website: https://univ-cotedazur.eu/antidote
• Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR

Example of Document in Antidote CasiMedicos Dataset

Structure of CasiMedicos Dataset

id: question id,
text: tokenized text for the argument component detection (list of lists)
labels: labels for the argument component detection (list of lists)
relations: relations between arguments (list of <argument1, argument2, relation> triplets)

Results of Argument Component Detection using LLMs

Citation

If you use CasiMedicos-Arg then please cite the following paper:

@inproceedings{sviridova-etal-2024-casimedicos,
    title = {{CasiMedicos-Arg: A Medical Question Answering Dataset Annotated with Explanatory Argumentative Structures}},
    author = "Sviridova, Ekaterina  and
      Yeginbergen, Anar  and
      Estarrona, Ainara  and
      Cabrio, Elena  and
      Villata, Serena  and
      Agerri, Rodrigo",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    year = "2024",
    url = "https://aclanthology.org/2024.emnlp-main.1026",
    pages = "18463--18475"
}

Contact: Rodrigo Agerri HiTZ Center - Ixa, University of the Basque Country UPV/EHU