ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials
Abstract
Evidence-based medicine aims at making decisions about the care of individual patients based on the explicit use of the best available evidence in the patient clinical history and the medical literature results. Argumentation represents a natural way of addressing this task by (i) identifying evidence and claims in text, and (ii) reasoning upon the extracted arguments and their relations to make a decision. ACTA 2.0 is an automated tool which relies on Argument Mining methods to analyse the abstracts of clinical trials to extract argument components and relations to support evidence-based clinical decision making. ACTA 2.0 allows also for the identification of PICO (Patient, Intervention, Comparison, Outcome) elements, and the analysis of the effects of an intervention on the outcomes of the study. A REST API is also provided to exploit the tool’s functionalities.
Cite
Text
Molinet et al. "ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials." International Joint Conference on Artificial Intelligence, 2022. doi:10.24963/IJCAI.2022/859Markdown
[Molinet et al. "ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials." International Joint Conference on Artificial Intelligence, 2022.](https://mlanthology.org/ijcai/2022/molinet2022ijcai-acta/) doi:10.24963/IJCAI.2022/859BibTeX
@inproceedings{molinet2022ijcai-acta,
title = {{ACTA 2.0: A Modular Architecture for Multi-Layer Argumentative Analysis of Clinical Trials}},
author = {Molinet, Benjamin and Marro, Santiago and Cabrio, Elena and Villata, Serena and Mayer, Tobias},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2022},
pages = {5940-5943},
doi = {10.24963/IJCAI.2022/859},
url = {https://mlanthology.org/ijcai/2022/molinet2022ijcai-acta/}
}