Harmonization of conflicting medical opinions using argumentation protocols and textual entailment - a case study on Parkinson disease

TL;DR

This paper presents a multi-agent framework that uses argumentation protocols, textual entailment, and ontologies to analyze and harmonize conflicting medical opinions in Parkinson's disease research.

Contribution

It introduces a novel combination of NLP, ontologies, and argumentation protocols to identify and resolve conflicts in medical literature on Parkinson's disease.

Findings

Effective identification of conflicting medical opinions

Successful integration of ontologies with natural language text

Harmonization of disagreements through mediation protocols

Abstract

Parkinson's disease is the second most common neurodegenerative disease, affecting more than 1.2 million people in Europe. Medications are available for the management of its symptoms, but the exact cause of the disease is unknown and there is currently no cure on the market. To better understand the relations between new findings and current medical knowledge, we need tools able to analyse published medical papers based on natural language processing and tools capable to identify various relationships of new findings with the current medical knowledge. Our work aims to fill the above technological gap. To identify conflicting information in medical documents, we enact textual entailment technology. To encapsulate existing medical knowledge, we rely on ontologies. To connect the formal axioms in ontologies with natural text in medical articles, we exploit ontology verbalisation techniques. To assess the level of disagreement between human agents with respect to a medical issue, we rely on fuzzy aggregation. To harmonize this disagreement, we design mediation protocols within a multi-agent framework.