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dc.contributor.authorVasiljeva, Ieva
dc.contributor.authorArandelovic, Ognjen
dc.date.accessioned2016-07-09T23:31:40Z
dc.date.available2016-07-09T23:31:40Z
dc.date.issued2016-07-10
dc.identifier.citationVasiljeva , I & Arandelovic , O 2016 , Automatic knowledge extraction from EHRs . in IJCAI 2016 - Workshop on Knowledge Discovery in Healthcare Data : New York City, USA, 10 July 2016 . IJCAI 2016 - Workshop on Knowledge Discovery in Healthcare Data , New York , United States , 10/07/16 . < https://sites.google.com/site/ijcai2016kdhealth/accepted-papers >en
dc.identifier.citationworkshopen
dc.identifier.otherPURE: 243452345
dc.identifier.otherPURE UUID: 505b7db6-806f-4bbc-a949-eef6dd41c900
dc.identifier.urihttps://hdl.handle.net/10023/9104
dc.description.abstractIncreasing efforts in the collection, standardization, and maintenance of large scale longitudinal elec- tronic health care records (EHRs) across the world provide a promising source of real world medical data with the potential of providing major novel insights of benefit both to specific individuals in the context of personalized medicine, as well as on the level of population-wide health care and policy. The present paper builds upon the existing and intensifying efforts at using machine learning to provide predictions on future diagnoses likely to be experienced by a particular individual based on the person’s existing diagnostic history. The specific model adopted as the baseline predictive framework is based on the concept of a binary diagnostic history vector representation of a patient’s diagnostic medical record. The technical novelty introduced herein concerns the manner in which transitions between diagnostic history vectors are learnt. We demonstrate that the proposed change prima fasciae enables greater learning specificity. We present a series of experiments which demon- strate the effectiveness of the proposed techniques, and which reveal novel insights regarding the most promising future research directions.
dc.format.extent6
dc.language.isoeng
dc.relation.ispartofIJCAI 2016 - Workshop on Knowledge Discovery in Healthcare Dataen
dc.rights© 2016, the Author(s). This work is made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version.en
dc.subjectRA0421 Public health. Hygiene. Preventive Medicineen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectNDASen
dc.subjectSDG 3 - Good Health and Well-beingen
dc.subject.lccRA0421en
dc.subject.lccQA75en
dc.titleAutomatic knowledge extraction from EHRsen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.urlhttps://sites.google.com/site/ijcai2016kdhealth/accepted-papersen


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