SurfCon: Synonym Discovery on Privacy-Aware Clinical Data
| Autor: | Zhen Wang, Simon Lin, Huan Sun, Xiang Yue, Soheil Moosavinasab, Yungui Huang |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Computation and Language Information retrieval Synonym Computer science Context (language use) 02 engineering and technology 3. Good health Task (project management) Term (time) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computation and Language (cs.CL) |
| Zdroj: | KDD |
| DOI: | 10.48550/arxiv.1906.09285 |
| Popis: | 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. Comment: KDD 2019 (Accepted for Oral Presentation at the Research track) |
| Databáze: | OpenAIRE |
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