Evidence-Based Treatment of Medical Guideline

  • Pingfang Tian
  • Zhonghua ZhuEmail author
  • Zhisheng Huang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 480)


Medical guidelines are recommendations on the appropriate treatment and care of people with specific diseases and conditions. Evidence-based medical guidelines are the document or recommendations which have been annotated with their relevant medical evidences, namely research findings from medical publications. We have observed the fact that there exist significant amount of medical guidelines have not yet annotated with relevant medical evidences, which becomes even more serious in the Chinese medical guidelines. In this paper, we propose an approach of evidence process of medical guidelines, such that we can find relevant evidences for those non-evidence-based medical guidelines. We develop a system called Link2Pubmed, which can retrieve the text which is described with a natural language and get the corresponding medical evidences. We use the word segmentation and part-of-speech tagging tools in natural language processing (NLP) to extract the keywords, and then translate them into corresponding English concepts in SNOMED CT, a well-known medical ontology. This system is an attempt to solve the existing problems in Chinese medical guidelines, which lack the annotations of relevant evidences.


Medical guideline NLP SNOMED CT PubMed Link2Pubmed 



This work was partially supported by a grant from the NSF (Natural Science Foundation) of China under grant number 60803160 and 61272110, the Key Projects of National Social Science Foundation of China under grant number 11&ZD189, and it was partially supported by a grant from NSF of Hubei Prov. of China under grant number 2013CFB334. It was partially supported by NSF of educational agency of Hubei Prov. under grant number Q20101110, and the State Key Lab of Software Engineering Open Foundation of Wuhan University under grant number SKLSE2012-09-07.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.Hubei Province Key Laboratory of Intelligent Information Processing and Realtime Industrial SystemWuhanPeople’s Republic of China
  3. 3.Department of Computer ScienceVrije University of AmsterdamAmsterdamThe Netherlands

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