用自然语言推理技术在数据少时提取医学论断关系
Medical Argument Mining: Exploitation of Scarce Data Using NLI Systems
- 结合分词分类与自然语言推理识别临床文本论点
- 数据稀缺下表现优于传统文本分类方法
- 适合医疗AI可解释性研究者参考
本研究提出一种论点挖掘方法，通过分词分类与自然语言推理技术从临床文本中提取论点实体及其关系。相比直接使用文本分类的方法，该方法在数据稀缺场景下表现出更优性能。通过评估该方法在识别支持或反驳诊断的论点结构方面的有效性，为未来提供机器生成临床结论的证据依据工具奠定了基础。
原文摘要 · Abstract (English)
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.
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