Bridging the vocabulary gap between health seekers and healthcare knowledge

Liqiang Nie, Yi Liang Zhao, Jialie Shen, Tat Seng Chua

Research output: Contribution to journalArticle

72 Citations (Scopus)

Abstract

The vocabulary gap between health seekers and providers has hindered the cross-system operability and the inter-user reusability. To bridge this gap, this paper presents a novel scheme to code the medical records by jointly utilizing local mining and global learning approaches, which are tightly linked and mutually reinforced. Local mining attempts to code the individual medical record by independently extracting the medical concepts from the medical record itself and then mapping them to authenticated terminologies. A corpus-aware terminology vocabulary is naturally constructed as a byproduct, which is used as the terminology space for global learning. Local mining approach, however, may suffer from information loss and lower precision, which are caused by the absence of key medical concepts and the presence of irrelevant medical concepts. Global learning, on the other hand, works towards enhancing the local medical coding via collaboratively discovering missing key terminologies and keeping off the irrelevant terminologies by analyzing the social neighbors. Comprehensive experiments well validate the proposed scheme and each of its component. Practically, this unsupervised scheme holds potential to large-scale data.

Original languageEnglish
Article number6843980
Pages (from-to)396-409
JournalIEEE Transactions on Knowledge and Data Engineering
Volume27
Issue number2
DOIs
Publication statusPublished - 25 Jun 2015
Externally publishedYes

Keywords

  • global learning
  • Healthcare
  • local mining
  • medical terminology assignment
  • question answering

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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