Detecting New Evidence for Evidence-Based Guidelines Using a Semantic Distance Method

Hu, Qing; Huang, Zhisheng; Teije, Annette ten; Harmelen, Frank van

doi:10.1007/978-3-319-19551-3_39

Cited by 5 publications

(21 citation statements)

References 7 publications

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“…In (Hu et al, 2015), we propose a semantic distance measure to rank terms for finding relevant evidence from a Biomedical search engine such as PubMed. Our semantic distance measure is based on the (widely shared) assumption that more frequently co-occurring terms are more semantically related.…”

Section: Present the Best Results To The Usermentioning

confidence: 99%

“…In (Hu et al, 2015), we have reported several experiments to evaluate the above approach. We selected the Dutch breast cancer guideline (version 1.0, 2004) (NABON, 2004) and the Dutch breast cancer guideline (version 2.0, 2012) (NABON, 2012) as the test data.…”

Section: Present the Best Results To The Usermentioning

confidence: 99%

“…From these experiments, we found that there is room to improve the search results by reducing the sizes of the returned results and to find more goal evidence for more guideline items. In (Hu et al, 2015) and as explained above, the center term is defined as the term for which the average distance to other terms is minimal. That definition of center term is independent of the topic of a selected guideline conclusion (where by topic, we mean the titles of the sections or subsections in which the guideline conclusions are contained).…”

Section: Present the Best Results To The Usermentioning

confidence: 99%

“…In our experiments reported in (Hu et al, 2015), we use Xerox's NLP tool (Ait-Mokhtar et al, 2013;Aït-Mokhtar et al, 2002) to identify the medical terms from UMLS and SNOMED CT which appear in guideline statements , and then use these terms to construct a PubMed query to search for relevant evidence. The resulting PubMed ID (alternatively called PMID) can serve as the ID of a retrieved evidence.…”

Section: Guideline Updatesmentioning

confidence: 99%

“…The experiment of that approach shows the recall is excellent, but the precision is very low (10.000 articles contain only 7 goal articles) (Reinders et al, 2015). In (Hu et al, 2015), we propose a method that uses a semantic distance measure to automatically find relevant new evidence for guideline updates. The advantage of using semantic distance is that the relevance measure can be achieved via the co-occurrence of terms in a biomedical article, which can be easily obtained via a biomedical search engine such as PubMed, instead of gathering a large corpus for the analysis.…”

Section: Introductionmentioning

confidence: 99%

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A Topic-centric Approach to Detecting New Evidences for Evidence-based Medical Guidelines

Hu¹,

Huang²,

Teije³

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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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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Section: Present the Best Results To The Usermentioning

confidence: 99%

Section: Present the Best Results To The Usermentioning

confidence: 99%

Section: Present the Best Results To The Usermentioning

confidence: 99%

Section: Guideline Updatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Topic-centric Approach to Detecting New Evidences for Evidence-based Medical Guidelines

Hu¹,

Huang²,

Teije³

et al. 2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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Huang

Wang

et al. 2019

Health Information Science

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2017

Biomedical Engineering Systems and Technologies

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Detecting New Evidence for Evidence-Based Guidelines Using a Semantic Distance Method

Cited by 5 publications

References 7 publications

A Topic-centric Approach to Detecting New Evidences for Evidence-based Medical Guidelines

A Topic-centric Approach to Detecting New Evidences for Evidence-based Medical Guidelines

Semantic Processing of Personality Description for Depression Patients

Accelerating the Exploitation of (bio)medical Knowledge Using Linked Data

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