Medical Argument Mining: Exploitation of Scarce Data Using NLI Systems

2025-06-15

AI Summary

Extracting argumentative entities and identifying support/refute relations in clinical texts—critical for diagnostic reasoning—remains challenging under data-scarce conditions. Method: We propose an end-to-end argument structure parsing framework that jointly leverages token-level sequence labeling and a pre-trained natural language inference (NLI) model (e.g., RoBERTa-MNLI). This is the first work to adapt the NLI paradigm to medical argument mining, moving beyond conventional classification-based approaches. Contribution/Results: Our method significantly improves few-shot relation classification performance by exploiting entailment semantics between argument components. Evaluated on a real-world clinical note dataset, it achieves an F1 score of 78.3%, outperforming strong baselines by 12.6 percentage points. The approach enhances the interpretability and evidential grounding of automated diagnostic conclusions, establishing a novel paradigm for low-resource medical NLP tasks.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Extracts argumentative entities from clinical texts
• Identifies relationships using NLI and token classification
• Improves performance in data-scarce medical settings

Innovation

• Token classification for argument extraction
• NLI techniques for relationship identification
• Superior performance in scarce data settings