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Knowledge-Driven Paper Retrieval to Support Updating of Clinical Guidelines

A Use Case on PubMed
  • Veruska ZamborliniEmail author
  • Qing Hu
  • Zhisheng Huang
  • Marcos da Silveira
  • Cedric Pruski
  • Annette ten Teije
  • Frank van Harmelen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10096)

Abstract

Clinical Guidelines are important knowledge resources for medical decision making. They provide clinical recommendations based on a collection of research findings with respect to a specific disease. Since, new findings are regularly published, CGs are also expected to be regularly updated. However, selecting and analysing medical publications require a huge human efforts, even when these publications are mostly regrouped and into repositories (e.g., MEDLINE database) and accessible via a search engine (e.g. PubMed). Automatically detecting those research findings from a medical search engine such as PubMed supports the guideline updating process. A simple search method is to select the medical terms that appear in the conclusions of the guideline to generate a query to search for new evidences. However, some challenges rise in this method: how to select the important terms, besides how to consider background knowledge that may be missing or not explicitly stated in those conclusions. In this paper we apply a knowledge model that formally describes elements such as actions and their effects to investigate (i) if it favors selecting the medical terms to compose queries and (ii) if a search enhanced with background knowledge can provide better result than other methods. This work explores a knowledge-driven approach for detecting new evidences relevant for the clinical guideline update process. Based on the outcomes of two experiments, we found that this approach can improve the recall by retrieving more relevant evidences than previous methods.

Keywords

Breast Reconstruction Semantic Distance Alternative Description SPARQL Query Silicon Implant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Veruska Zamborlini
    • 1
    • 2
    Email author
  • Qing Hu
    • 1
  • Zhisheng Huang
    • 1
  • Marcos da Silveira
    • 2
  • Cedric Pruski
    • 2
  • Annette ten Teije
    • 1
  • Frank van Harmelen
    • 1
  1. 1.Vrije Universiteit AmsterdamAmsterdamThe Netherlands
  2. 2.Luxembourg Institute of Science and Technology - LISTEsch-sur-AlzetteLuxembourg

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