Unstructured clinical texts contain rich health-related information. To
better utilize the knowledge buried in clinical texts, discovering synonyms for
a medical query term has become an important task. Recent automatic synonym
discovery methods leveraging raw text information have been developed. However,
to preserve patient privacy and security, it is usually quite difficult to get
access to large-scale raw clinical texts. In this paper, we study a new setting
named synonym discovery on privacy-aware clinical data (i.e., medical terms
extracted from the clinical texts and their aggregated co-occurrence counts,
without raw clinical texts). To solve the problem, we propose a new framework
SurfCon that leverages two important types of information in the privacy-aware
clinical data, i.e., the surface form information, and the global context
information for synonym discovery. In particular, the surface form module
enables us to detect synonyms that look similar while the global context module
plays a complementary role to discover synonyms that are semantically similar
but in different surface forms, and both allow us to deal with the OOV query
issue (i.e., when the query is not found in the given data). We conduct
extensive experiments and case studies on publicly available privacy-aware
clinical data, and show that SurfCon can outperform strong baseline methods by
large margins under various settings.

本文提出了一种新的架构，名为 “SurfCon”，用于在隐私保护的临床数据（即从临床文本中提取的医学术语及其聚合共现计数，而不需要使用原始的临床文本）上发现医学查询词的同义词，并通过实验表明该方法能够在不同的设置下优于强基线方法。