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。

无监督的临床笔记片段提取、标记和聚类

Unsupervised extraction, labelling and clustering of segments from clinical notes

The task of identifying synonymous relations and objects, or synonym
resolution, is critical for high-quality information extraction. This paper
investigates synonym resolution in the context of unsupervised information
extraction, where neither hand-tagged training examples nor domain knowledge is
available. The paper presents a scalable, fully-implemented system that runs in
O(KN log N) time in the number of extractions, N, and the maximum number of
synonyms per word, K. The system, called Resolver, introduces a probabilistic
relational model for predicting whether two strings are co-referential based on
the similarity of the assertions containing them. On a set of two million
assertions extracted from the Web, Resolver resolves objects with 78% precision
and 68% recall, and resolves relations with 90% precision and 35% recall.
Several variations of resolvers probabilistic model are explored, and
experiments demonstrate that under appropriate conditions these variations can
improve F1 by 5%. An extension to the basic Resolver system allows it to handle
polysemous names with 97% precision and 95% recall on a data set from the TREC
corpus.

本研究探讨了一种无监督信息提取场景下的同义词识别方法，它引入了可扩展的基于概率关系模型的 Resolver 系统，其在使用来自网络的两百万断言集进行测试时，对于确定同一对象和关系的任务，分别具有 78% 和 90% 的准确度和 68% 和 35% 的召回率，而且可以通过一些变化提高效果。此系统还扩展了处理多义名词的能力。