Medical Argument Mining: Exploitation of Scarce Data Using NLI Systems
This research addresses the challenge of providing evidence-based justifications for machine-generated clinical conclusions, which is incremental as it builds on existing NLI techniques for a specific domain.
The paper tackled the problem of extracting argumentative entities and their relationships from clinical texts using token classification and Natural Language Inference, achieving superior performance in data-scarce settings compared to straightforward methods like text classification.
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.