Next Article in Journal
Field Test of a Remote Multi-Path CLaDS Methane Sensor
Next Article in Special Issue
Optimized ECC Implementation for Secure Communication between Heterogeneous IoT Devices
Previous Article in Journal
A Ratiometric Wavelength Measurement Based on a Silicon-on-Insulator Directional Coupler Integrated Device
Previous Article in Special Issue
An Intelligent System Proposal for Improving the Safety and Accessibility of Public Transit by Highway
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(9), 21294-21314; doi:10.3390/s150921294

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

1
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea
2
Department of Computing and Information Systems, University of Tasmania, Hobart 7001, Australia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Jesús Fontecha and Paul Mccullagh
Received: 13 May 2015 / Revised: 16 August 2015 / Accepted: 25 August 2015 / Published: 28 August 2015
View Full-Text   |   Download PDF [3389 KB, uploaded 28 August 2015]   |  

Abstract

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. View Full-Text
Keywords: automated query construction; knowledge-based queries; CDSS; Arden Syntax; medical logic modules automated query construction; knowledge-based queries; CDSS; Arden Syntax; medical logic modules
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Afzal, M.; Hussain, M.; Ali, T.; Hussain, J.; Khan, W.A.; Lee, S.; Kang, B.H. Knowledge-Based Query Construction Using the CDSS Knowledge Base for Efficient Evidence Retrieval. Sensors 2015, 15, 21294-21314.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top