论文标题
计算乳腺癌筛查指南之间的概念距离：实施近点的医学分歧模型
Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement
论文作者
论文摘要
使用自然语言处理工具，我们研究了有关同一决策问题的医疗指南中建议的差异 - 乳腺癌筛查。我们表明，这些差异是由不同的医学社会带来的知识引起的，这反映在不同作者组使用的概念词汇中。即使本文是一项案例研究，旨在介绍一组准则，但提出的方法通常适用。除了提出一个基于图形的新型相似性模型以比较文档的集合外，我们还对模型性能进行了广泛的分析。在几十个实验中，在三个广泛的类别中，我们以最佳模型的3-4个标准偏差的高度统计显着性水平表明，专家注释的模型与基于概念的自动创建的高度相似性并不是偶然的。我们的最佳模型可实现约70％的相似性。我们还描述了这项工作的可能扩展。
Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem -- breast cancer screening. We show that these differences arise from knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors.The computational models we build and analyze agree with the near-peer epistemic model of expert disagreement proposed by Garbayo. Even though the article is a case study focused on one set of guidelines, the proposed methodology is broadly applicable. In addition to proposing a novel graph-based similarity model for comparing collections of documents, we perform an extensive analysis of the model performance. In a series of a few dozen experiments, in three broad categories, we show, at a very high statistical significance level of 3-4 standard deviations for our best models, that the high similarity between expert annotated model and our concept based, automatically created, computational models is not accidental. Our best model achieves roughly 70% similarity. We also describe possible extensions of this work.
