Dynamic Service Detection for Automated Patient Selection for Study Recruitment Purposes

Chondrogiannis, Efthymios; Andronikou, Vassiliki; Karanastasis, Efstathios; Varvarigou, Theodora

dc.contributor.author	Chondrogiannis, Efthymios
dc.contributor.author	Andronikou, Vassiliki
dc.contributor.author	Karanastasis, Efstathios
dc.contributor.author	Varvarigou, Theodora
dc.date.accessioned	2021-01-07T15:27:01Z
dc.date.available	2021-01-07T15:27:01Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/52733
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.20431
dc.relation	info:eu-repo/grantAgreement/EC/H2020/731944	el
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	automated patient selection	en
dc.subject	service-oriented architecture	en
dc.subject	dynamic service detection	en
dc.subject	semantic web	en
dc.subject	clinical trials	en
dc.title	Dynamic Service Detection for Automated Patient Selection for Study Recruitment Purposes	en
heal.type	conferenceItem
heal.classification	computer science	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2018-07-05
heal.bibliographicCitation	E. Chondrogiannis, V. Andronikou, E. Karanastasis and T. Varvarigou, "Dynamic Service Detection for Automated Patient Selection for Study Recruitment Purposes," 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW), Paris, 2018, pp. 72-77, doi: 10.1109/ICDEW.2018.00019.	en
heal.abstract	ICT-enabled automated processes facilitating subject recruitment in clinical trials is a hot topic in the clinical research domain. However, the successful detection of candidate subjects across patient records of different healthcare providers requires dealing with a variety of issues, including but not limited to different terminologies and structures as well as missing patient data. In this work a novel system for automated patient selection for study recruitment purposes is being presented. The system is based on the dynamic service detection for eligibility criteria evaluation, while the patient data retrieved from each healthcare entity are further processed for clinical research purposes. The presented system is also compared with a different framework that we have already developed in the past that is based on query rewriting and translation for eligibility criteria evaluation purposes. The advantages and limitations of the two aforementioned approaches are being discussed as well as their potential combination for automated patient selection.	en
heal.sponsor	This work has been partially funded by the European Commission’s activity of the Horizon 2020 project HarmonicSS under contract number 731944.This paper expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this paper.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)	en
heal.conferenceItemType	full paper
dc.identifier.doi	10.1109/ICDEW.2018.00019	el