Discovering novel causal patterns from biomedical natural-language texts using Bayesian nets

John Atkinson, Alejandro Rivas

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Most of the biomedicine text mining approaches do not deal with specific cause - effect patterns that may explain the discoveries. In order to fill this gap, this paper proposes an effective new model for text mining from biomedicine literature that helps to discover cause - effect hypotheses related to diseases, drugs, etc. The supervised approach combines Bayesian inference methods with natural-language processing techniques in order to generate simple and interesting patterns. The results of applying the model to biomedicine text databases and its comparison with other state-of-the-art methods are also discussed.

Original languageEnglish
Pages (from-to)714-722
Number of pages9
JournalIEEE Transactions on Information Technology in Biomedicine
Volume12
Issue number6
DOIs
StatePublished - 2008
Externally publishedYes

Keywords

  • Bayesian nets
  • Biomedicine
  • Information extraction
  • Knowledge discovery
  • Text mining

Fingerprint

Dive into the research topics of 'Discovering novel causal patterns from biomedical natural-language texts using Bayesian nets'. Together they form a unique fingerprint.

Cite this