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Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval

Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construc...

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Detalles Bibliográficos
Autores principales: Afzal, Muhammad, Hussain, Maqbool, Ali, Taqdir, Hussain, Jamil, Khan, Wajahat Ali, Lee, Sungyoung, Kang, Byeong Ho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610474/
https://www.ncbi.nlm.nih.gov/pubmed/26343669
http://dx.doi.org/10.3390/s150921294
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author Afzal, Muhammad
Hussain, Maqbool
Ali, Taqdir
Hussain, Jamil
Khan, Wajahat Ali
Lee, Sungyoung
Kang, Byeong Ho
author_facet Afzal, Muhammad
Hussain, Maqbool
Ali, Taqdir
Hussain, Jamil
Khan, Wajahat Ali
Lee, Sungyoung
Kang, Byeong Ho
author_sort Afzal, Muhammad
collection PubMed
description Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important.
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spelling pubmed-46104742015-10-26 Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval Afzal, Muhammad Hussain, Maqbool Ali, Taqdir Hussain, Jamil Khan, Wajahat Ali Lee, Sungyoung Kang, Byeong Ho Sensors (Basel) Article Finding appropriate evidence to support clinical practices is always challenging, and the construction of a query to retrieve such evidence is a fundamental step. Typically, evidence is found using manual or semi-automatic methods, which are time-consuming and sometimes make it difficult to construct knowledge-based complex queries. To overcome the difficulty in constructing knowledge-based complex queries, we utilized the knowledge base (KB) of the clinical decision support system (CDSS), which has the potential to provide sufficient contextual information. To automatically construct knowledge-based complex queries, we designed methods to parse rule structure in KB of CDSS in order to determine an executable path and extract the terms by parsing the control structures and logic connectives used in the logic. The automatically constructed knowledge-based complex queries were executed on the PubMed search service to evaluate the results on the reduction of retrieved citations with high relevance. The average number of citations was reduced from 56,249 citations to 330 citations with the knowledge-based query construction approach, and relevance increased from 1 term to 6 terms on average. The ability to automatically retrieve relevant evidence maximizes efficiency for clinicians in terms of time, based on feedback collected from clinicians. This approach is generally useful in evidence-based medicine, especially in ambient assisted living environments where automation is highly important. MDPI 2015-08-28 /pmc/articles/PMC4610474/ /pubmed/26343669 http://dx.doi.org/10.3390/s150921294 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Afzal, Muhammad
Hussain, Maqbool
Ali, Taqdir
Hussain, Jamil
Khan, Wajahat Ali
Lee, Sungyoung
Kang, Byeong Ho
Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval
title Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval
title_full Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval
title_fullStr Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval
title_full_unstemmed Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval
title_short Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval
title_sort knowledge-based query construction using the cdss knowledge base for efficient evidence retrieval
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4610474/
https://www.ncbi.nlm.nih.gov/pubmed/26343669
http://dx.doi.org/10.3390/s150921294
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