Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit

dc.contributor.author	Σιδηρόπουλος, Κωνσταντίνος	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.date.accessioned	2015-05-04T08:13:09Z
dc.date.available	2015-05-04T08:13:09Z
dc.date.issued	2015-05-04
dc.identifier.uri	http://hdl.handle.net/11400/9613
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S0010482511002381	en
dc.subject	Decision support systems
dc.subject	Rare brain cancers
dc.subject	Σύστημα υποστήριξης αποφάσεων
dc.subject	Σπάνιες μορφές καρκίνου του εγκεφάλου
dc.title	Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://lod.nal.usda.gov/31914
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.contributorName	Stonham, John	en
heal.identifier.secondary	doi:10.1016/j.compbiomed.2011.12.004
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2012
heal.bibliographicCitation	Sidiropoulos, K., Glotsos, D., Kostopoulos, S., Ravazoula, P., Kalatzis, I., et al. (April 2012). Real time decision support system for diagnosis of rare cancers, trained in parallel, on a graphics processing unit. Computers in Biology and Medicine. 42(4). pp. 376-386. Elsevier Ltd: 2012. Available from: http://www.sciencedirect.com/science/article/pii/S0010482511002381 [Accessed 23/12/2011]	en
heal.abstract	In the present study a new strategy is introduced for designing and developing of an efficient dynamic Decision Support System (DSS) for supporting rare cancers decision making. The proposed DSS operates on a Graphics Processing Unit (GPU) and it is capable of adjusting its design in real time based on user-defined clinical questions in contrast to standard CPU implementations that are limited by processing and memory constrains. The core of the proposed DSS was a Probabilistic Neural Network classifier and was evaluated on 140 rare brain cancer cases, regarding its ability to predict tumors' malignancy, using a panel of 20 morphological and textural features Generalization was estimated using an external 10-fold cross-validation. The proposed GPU-based DSS achieved significantly higher training speed, outperforming the CPU-based system by a factor that ranged from 267 to 288 times. System design was optimized using a combination of 4 textural and morphological features with 78.6% overall accuracy, whereas system generalization was 73.8%±3.2%. By exploiting the inherently parallel architecture of a consumer level GPU, the proposed approach enables real time, optimal design of a DSS for any user-defined clinical question for improving diagnostic assessments, prognostic relevance and concordance rates for rare cancers in clinical practice.	en
heal.publisher	Elsevier Ltd	en
heal.journalName	Computers in Biology and Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true