• DocumentCode
    2938432
  • Title

    Similarity retrieval of cardiac reports

  • Author

    Syeda-Mahmood, Tanveer

  • Author_Institution
    IBM Almaden Res. Center, San Jose, CA, USA
  • fYear
    2010
  • fDate
    Aug. 31 2010-Sept. 4 2010
  • Firstpage
    1135
  • Lastpage
    1141
  • Abstract
    Mining medical reports can reveal important information correlating diagnosis with raw measurements helping in decision support. In this paper we address the problem of finding similar measurement reports for aiding clinical decision support. Specifically, we present a new approach to generating document class models for measurement reports using a multi-scale feature-value kernel. The class models serve as natural feature selection mechanism as well as indexes to large report collections. A document retrieval algorithm based on document class models is presented to enable similarity retrieval of pre-diagnosed reports. Collaborative filtering-guided assembly of associated disease labels is used to achieve clinical decision support.
  • Keywords
    data mining; diseases; medical information systems; cardiac reports; clinical decision support; collaborative filtering-guided assembly; disease labels; document retrieval algorithm; medical reports; multiscale feature-value kernel; natural feature selection mechanism; pre-diagnosed reports; Databases; Diseases; Feature extraction; Histograms; Kernel; Mathematical model; Training; California; Cardiovascular Diseases; Data Mining; Database Management Systems; Decision Support Systems, Clinical; Electronic Health Records; Forms and Records Control; Humans; Medical Record Linkage; Natural Language Processing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
  • Conference_Location
    Buenos Aires
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-4123-5
  • Type

    conf

  • DOI
    10.1109/IEMBS.2010.5627132
  • Filename
    5627132