Similarity-based recommendation of new concepts to a terminology

Praveen Chandar, Anil Yaman, Julia Hoxha, Zhe He, Chunhua Weng

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

Terminologies can suffer from poor concept coverage due to delays in addition of new concepts. This study tests a similarity-based approach to recommending concepts from a text corpus to a terminology. Our approach involves extraction of candidate concepts from a given text corpus, which are represented using a set of features. The model learns the important features to characterize a concept and recommends new concepts to a terminology. Further, we propose a cost-effective evaluation methodology to estimate the effectiveness of terminology enrichment methods. To test our methodology, we use the clinical trial eligibility criteria free-text as an example text corpus to recommend concepts for SNOMED CT. We computed precision at various rank intervals to measure the performance of the methods. Results indicate that our automated algorithm is an effective method for concept recommendation.

Original languageEnglish
Pages (from-to)386-35
Number of pages10
JournalAMIA Annual Symposium Proceedings
Volume2015
Publication statusPublished - 1 Jan 2015

Keywords

  • Algorithms
  • Computer Simulation
  • Information Storage and Retrieval/methods
  • Systematized Nomenclature of Medicine
  • Terminology as Topic

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    Chandar, P., Yaman, A., Hoxha, J., He, Z., & Weng, C. (2015). Similarity-based recommendation of new concepts to a terminology. AMIA Annual Symposium Proceedings, 2015, 386-35.