Knowledge extraction and mining in biomedical research using rule network model

S. W. Chan, Clement H C LEUNG, A. Milani

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

1 Citation (Scopus)

Abstract

Recent findings show that the quantity of published biomedical literature is increasing at a dramatic rate. Carrying out knowledge extraction from large amounts of research literature becomes a significant challenge. Here we introduce an automatic mechanism for processing such information and extracting meaningful medical knowledge from biomedical literature. Data mining and natural language processing (NLP) are applied in a novel model, called biomedical rule network model. Using this model, information and relationships among herbal materials and diseases, as well as the chemical constituents of herbs can be extracted automatically. Moreover, with the overlapping chemical constituents of herbs, alternative herbal materials can be discovered, and suggestions can be made to replace expensive treatment options with lower cost ones.

Original languageEnglish
Title of host publicationBrain and Health Informatics - International Conference, BHI 2013, Proceedings
Pages506-515
Number of pages10
DOIs
Publication statusPublished - 2013
EventInternational Conference on Brain and Health Informatics, BHI 2013 - Maebashi, Japan
Duration: 29 Oct 201331 Oct 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8211 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Brain and Health Informatics, BHI 2013
Country/TerritoryJapan
CityMaebashi
Period29/10/1331/10/13

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Biomedical literature
  • Chemical constituent
  • Herb
  • Hypothesis
  • Natural language processing

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