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Mining Professional Knowledge from Medical Records

  • Hen-Hsen Huang
  • Chia-Chun Lee
  • Hsin-Hsi Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8609)

Abstract

The paper aims at two tasks of electronic medical record (EMR) processing: EMR retrieval and medical term extraction. The linguistic phenomena in EMRs in different departments are analyzed in depth including record size, vocabulary, entropy of medical languages, grammaticality, and so on. We explore various techniques of information retrieval for EMR retrieval, including five retrieval models with six pre-processing strategies on different parts of EMRs. The learning to rank algorithm is also adopted to improve the retrieval performance. Finally, our retrieval model is applied to extract medical terms from EMRs. Both coarse-grained relevance evaluation on department level and fine-grained relevance evaluation on treatment level are conducted.

Keywords

Learning to Rank Medical Record Retrieval Professional Information Access 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Hen-Hsen Huang
    • 1
  • Chia-Chun Lee
    • 1
  • Hsin-Hsi Chen
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan

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