Authors:
Qing Hu
1
;
Zhisheng Huang
2
;
Annette ten Teije
2
;
Frank van Harmelen
2
;
M. Scott Marshall
3
and
Andre Dekker
3
Affiliations:
1
VU University Amsterdam and Wuhan Univesity of Science and Technology, Netherlands
;
2
VU University Amsterdam, Netherlands
;
3
Maastricht University Medical Centre, Netherlands
Keyword(s):
Evidence-based Medical Guidelines, Medical Guideline Update, Semantic Distance, Context-awareness, Topic-centric Approach.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Management
;
Medical and Nursing Informatics
;
Ontologies and the Semantic Web
;
Society, e-Business and e-Government
;
Web Information Systems and Technologies
Abstract:
Evidence-based Medical guidelines are developed based on the best available evidence in biomedical science and clinical practice. Such evidence-based medical guidelines should be regularly updated, so that they can optimally serve medical practice by using the latest evidence from medical research. The usual approach to detect such new evidence is to use a set of terms from a guideline recommendation and to create queries for a biomedical search engine such as PubMed, with a ranking over a selected subset of terms to search for relevant new evidence. However, the terms that appear in a guideline recommendation do not always cover all of the information we need for the search, because the contextual information (e.g. time and location, user profile, topics) is usually missing in a guideline recommendation. Enhancing the search terms with contextual information would improve the quality of the search results. In this paper, we propose a topic-centric approach to detect new evidence for
updating evidence-based medical guidelines as a context-aware method to improve the search. Our experiments show that this topic centric approach can find the goal evidence for 12 guideline statements out of 16 in our test set, compared with only 5 guideline statements that were found by using a non-topic centric approach.
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