Medical Argument Mining: Exploitation of Scarce Data Using NLI Systems

TL;DR

This paper introduces a novel argument mining approach for clinical texts that leverages Natural Language Inference to improve extraction of argumentative structures, especially in data-scarce scenarios, aiding evidence-based medical decision support.

Contribution

It proposes a new method combining token classification and NLI for argument mining in clinical texts, outperforming traditional text classification in low-data settings.

Findings

Superior performance in data-scarce environments

Effective identification of argumentative structures supporting diagnoses

Foundation for future clinical decision support tools

Abstract

This work presents an Argument Mining process that extracts argumentative entities from clinical texts and identifies their relationships using token classification and Natural Language Inference techniques. Compared to straightforward methods like text classification, this methodology demonstrates superior performance in data-scarce settings. By assessing the effectiveness of these methods in identifying argumentative structures that support or refute possible diagnoses, this research lays the groundwork for future tools that can provide evidence-based justifications for machine-generated clinical conclusions.