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Designing a Novel Framework for Precision Medicine Information Retrieval

  • Haihua Chen
  • Juncheng Ding
  • Jiangping ChenEmail author
  • Gaohui Cao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10983)

Abstract

Precision medicine information retrieval (PMIR) is about matching the most relevant scientific articles to an individual patient for reliable disease treatment. The corresponding Precision Medicine (PM) Track organized by 2017 Text REtrieval Conference [1] provides a test collection for evaluating the performance of PMIR techniques for finding reliable medical evidence. It significantly facilitates PMIR research and system development. However, the performance of current PMIR systems is still far from satisfactory. This study aims to investigate the application of the latest information retrieval and text mining techniques to PMIR. Based on a review of previous efforts and approaches, we propose three promising techniques: keyphrase extraction for indexing, hybrid query expansion including word embeddings, and retrieval results re-ranking with supervised regression analysis for PMIR. A novel framework for PMIR is therefore designed. A PMIR system based on this framework will be implemented and tested using 2017 and 2018 TREC Precision Medicine Track datasets.

Keywords

Precision medicine Information retrieval Keyphrase extraction Query expansion Supervised learning 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Information ScienceUniversity of North TexasDentonUSA
  2. 2.Department of Computer ScienceUniversity of North TexasDentonUSA
  3. 3.School of Information ManagementCentral China Normal UniversityWuhanChina

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