论文标题
从临床注释中无监督的提取，标记和聚类
Unsupervised extraction, labelling and clustering of segments from clinical notes
论文作者
论文摘要
这项工作是由缺乏工具的稀缺性来激发了从计算中代表性不足的语言（例如捷克语）中准确，无监督的信息提取的信息。我们将垫脚石引入了一系列下游任务，例如摘要或单个患者记录的整合，为国家癌症注册表报告的结构化信息提取或建立用于计算患者嵌入的半结构性语义患者表示。更具体地说，我们提出了一种方法，可以从临床注释中无监督提取语义标记的文本段，并在捷克乳腺癌患者的数据集中进行对其进行测试，该数据集由Masaryk Memoryk Memorial Cancer Institute（专门从事肿瘤学）提供。我们的目标是提取，分类（即标签）和簇段的自由文本注释，这些注释对应于特定的临床特征（例如家庭背景，合并症或毒性）。提出的结果证明了拟议方法的实际相关性，该方法是建立在捷克临床注释上部署的更复杂的提取和分析管道的实际相关性。
This work is motivated by the scarcity of tools for accurate, unsupervised information extraction from unstructured clinical notes in computationally underrepresented languages, such as Czech. We introduce a stepping stone to a broad array of downstream tasks such as summarisation or integration of individual patient records, extraction of structured information for national cancer registry reporting or building of semi-structured semantic patient representations for computing patient embeddings. More specifically, we present a method for unsupervised extraction of semantically-labelled textual segments from clinical notes and test it out on a dataset of Czech breast cancer patients, provided by Masaryk Memorial Cancer Institute (the largest Czech hospital specialising in oncology). Our goal was to extract, classify (i.e. label) and cluster segments of the free-text notes that correspond to specific clinical features (e.g., family background, comorbidities or toxicities). The presented results demonstrate the practical relevance of the proposed approach for building more sophisticated extraction and analytical pipelines deployed on Czech clinical notes.
