A distributed environment for the integration of multiple high-performance decision support systems into clinical workflow

Androulidakis, A; Nielsen, AD; Prentza, A; Koutsouris, D

dc.contributor.author	Androulidakis, A	en
dc.contributor.author	Nielsen, AD	en
dc.contributor.author	Prentza, A	en
dc.contributor.author	Koutsouris, D	en
dc.date.accessioned	2014-03-01T01:55:19Z
dc.date.available	2014-03-01T01:55:19Z
dc.date.issued	2006	en
dc.identifier.issn	09287329	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27687
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-33748482656&partnerID=40&md5=e7998d9f8faa6dbcade937d0bce92c3e	en
dc.subject	Causal probabilistic networks	en
dc.subject	Decision support systems	en
dc.subject	Distributed computing	en
dc.subject	Parallel algorithms	en
dc.subject.other	algorithm	en
dc.subject.other	antibiotic therapy	en
dc.subject.other	article	en
dc.subject.other	decision making	en
dc.subject.other	decision support system	en
dc.subject.other	human	en
dc.subject.other	mathematical computing	en
dc.subject.other	medical error	en
dc.subject.other	performance measurement system	en
dc.subject.other	physician	en
dc.subject.other	priority journal	en
dc.subject.other	system analysis	en
dc.subject.other	Algorithms	en
dc.subject.other	Computer Systems	en
dc.subject.other	Decision Support Systems, Clinical	en
dc.subject.other	Efficiency, Organizational	en
dc.subject.other	Humans	en
dc.subject.other	Neural Networks (Computer)	en
dc.subject.other	Software	en
dc.subject.other	Systems Integration	en
dc.subject.other	Time Factors	en
dc.title	A distributed environment for the integration of multiple high-performance decision support systems into clinical workflow	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.abstract	Current studies conclude that clinical decision support systems can help reduce serious medical errors. The importance of Causal Probabilistic Networks (CPNs) for constructing such systems is already well-known. However, the computational complexity of probabilistic inference, which results in unacceptably high response times, can hinder acceptance and integration into clinician workflow. This paper investigates the optimization and parallelization potential of complex CPN-based medical decision support systems and evaluates the results of implementing a parallel, high performance version of an existing decision support system concerning proper antibiotic treatment therapy. Furthermore, it discusses distributed computing techniques for making multiple high performance decision support systems available at the time and location of decision making, by exploiting computing resources residing inside, as well as outside the hospital walls optimally. © 2006 - IOS Press and the authors. All rights reserved.	en
heal.journalName	Technology and Health Care	en
dc.identifier.volume	14	en
dc.identifier.issue	3	en
dc.identifier.spage	157	en
dc.identifier.epage	170	en