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

We study the problem of troubleshooting machine learning systems that rely on
analytical pipelines of distinct components. Understanding and fixing errors
that arise in such integrative systems is difficult as failures can occur at
multiple points in the execution workflow. Moreover, errors can propagate,
become amplified or be suppressed, making blame assignment difficult. We
propose a human-in-the-loop methodology which leverages human intellect for
troubleshooting system failures. The approach simulates potential component
fixes through human computation tasks and measures the expected improvements in
the holistic behavior of the system. The method provides guidance to designers
about how they can best improve the system. We demonstrate the effectiveness of
the approach on an automated image captioning system that has been pressed into
real-world use.

研究如何解决机器学习系统中多个组件的分析流程中出现的问题，通过人力计算任务模拟潜在组件修复方案以改善系统整体行为，为设计师提供改进系统的指导，并在一个自动化图像字幕系统上演示了该方法的有效性。