SurfCon：隐私感知临床数据的同义词发现
SurfCon: Synonym Discovery on Privacy-Aware Clinical Data
摘要：
非结构化临床文本包含丰富的健康相关信息。为了更好地利用临床文本中隐藏的知识，发现医学查询词的同义词已成为一项重要任务。最近的自动同义词发现方法，利用原始文本信息已经开发出来。然而，为了保护患者的隐私和安全，通常很难访问大规模的原始临床文本。在本文中，我们研究了一种新的设置命名为同义词发现的隐私意识的临床数据（即，从临床文本中提取的医学术语及其聚集的共现计数，而没有原始临床文本）。为了解决这个问题，我们提出了一个新的框架SurfCon，它利用了隐私感知临床数据中的两种重要类型的信息，即，表面形式信息和用于同义词发现的全局上下文信息。特别地，表面形式模块使我们能够检测看起来相似的同义词，而全局上下文模块起补充作用以发现语义相似但表面形式不同的同义词，并且两者都允许我们处理OOV查询问题（即，当在给定数据中没有找到查询时）。我们对公开的隐私感知临床数据进行了广泛的实验和案例研究，并表明SurfCon可以在各种设置下大幅超越强基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
