• DocumentCode
    2036208
  • Title

    Relational learning from drug adverse events reports

  • Author

    Biçici, Ergun M.

  • Author_Institution
    Dept. of Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
  • fYear
    2004
  • fDate
    12-15 Oct. 2004
  • Firstpage
    77
  • Lastpage
    81
  • Abstract
    We applied relational learning to discover rules from adverse events reports. We used the FOIL relational learning system to find a set of rules for withdrawn drugs. We compared our results with FDA´s reasons for withdrawal.
  • Keywords
    drugs; learning (artificial intelligence); medical computing; FOIL relational learning system; drug adverse events reports; withdrawn drugs; Computer science; Demography; Drugs; Frequency; Industrial relations; Learning systems; Logic; Machine learning; Ontologies; Pharmaceuticals;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Biotechnology and Bioinformatics, 2004. Proceedings. Technology for Life: North Carolina Symposium on
  • Print_ISBN
    0-7803-8826-7
  • Type

    conf

  • DOI
    10.1109/SBB.2004.1364375
  • Filename
    1364375
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2036208