Argument Mining on Clinical Trials

Serena Villata 1, 2
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Argument-based decision making has been employed to support a variety of reasoning tasks over medical knowledge. These include evidence-based justifications of the effects of treatments, the detection of conflicts in the knowledge base, and the enabling of uncertain and defeasible reasoning in the health-care sector. However, a common limitation of these approaches is that they rely on structured input information. Recent advances in argument mining have shown increasingly accurate results in detecting argument components and predicting their relations from unstructured, natural language texts. In this study, we discuss evidence and claim detection from Randomized Clinical Trials. To this end, we create a new annotated dataset about four different diseases (glaucoma, diabetes, hepatitis B, and hypertension), containing 976 argument components (697 containing evidence, 279 claims). Empirical results are promising, and show the portability of the proposed approach over different branches of medicine.
Type de document :
Communication dans un congrès
COMMA 2018 - 7th International Conference on Computational Models of Argument Proceedings, Sep 2018, Warsaw, Poland. 305, pp.137 - 148, 2018, Frontiers in Artificial Intelligence and Applications
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https://hal.archives-ouvertes.fr/hal-01876498
Contributeur : Serena Villata <>
Soumis le : mardi 18 septembre 2018 - 14:49:14
Dernière modification le : vendredi 7 décembre 2018 - 15:18:18

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  • HAL Id : hal-01876498, version 1

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Serena Villata. Argument Mining on Clinical Trials. COMMA 2018 - 7th International Conference on Computational Models of Argument Proceedings, Sep 2018, Warsaw, Poland. 305, pp.137 - 148, 2018, Frontiers in Artificial Intelligence and Applications. 〈hal-01876498〉

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