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.

本文提出一种无监督的文本信息抽取方法，应用于捷克乳腺癌患者的临床记录中，以提取、分类和聚类具有特定临床特征的文本片段，证明提出方法在捷克样本中为构建更复杂的信息抽取和分析工具提供了实用价值。