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 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.
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. muhammad.afzal@oslab.khu.ac.kr.
Muhammad
M
Afzal
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. maqbool.hussain@oslab.khu.ac.kr.
Maqbool
M
Hussain
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. taqdir.ali@oslab.khu.ac.kr.
Taqdir
T
Ali
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. jamil@oslab.khu.ac.kr.
Jamil
J
Hussain
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. wajahat.alikhan@oslab.khu.ac.kr.
Wajahat Ali
WA
Khan
Department of Computer Engineering, Kyung Hee University, Seocheon-dong, Giheung-gu Yongin-si, Gyeonggi-do 446-701, Korea. sylee@oslab.khu.ac.kr.
Sungyoung
S
Lee
Department of Computing and Information Systems, University of Tasmania, Hobart 7001, Australia. byeong.kang@utas.adu.au.
Byeong Ho
BH
Kang
D001185:Artificial Intelligence; D040561:Assisted Living Facilities; D002908:Chronic Disease / Q000628:therapy; D020000:Decision Support Systems, Clinical; D057286:Electronic Health Records; D006699:Home Care Services; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D051188:Knowledge Bases; D016239:MEDLINE; D009369:Neoplasms / Q000628:therapy; D012984:Software
Arden Syntax; CDSS; automated query construction; knowledge-based queries; medical logic modules