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dc.contributor.authorVasiljeva, Ieva
dc.contributor.authorArandelovic, Ognjen
dc.date.accessioned2016-11-21T10:30:17Z
dc.date.available2016-11-21T10:30:17Z
dc.date.issued2016-08-16
dc.identifier247586510
dc.identifier7c1f2fd0-0830-437d-919b-dd1751eb7beb
dc.identifier85009126420
dc.identifier000399823502199
dc.identifier.citationVasiljeva , I & Arandelovic , O 2016 , Towards sophisticated learning from EHRs : increasing prediction specificity and accuracy using clinically meaningful risk criteria . in 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) . , 7591226 , IEEE , pp. 2452-2455 , 38th International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 , Orlando , United States , 16/08/16 . https://doi.org/10.1109/EMBC.2016.7591226en
dc.identifier.citationconferenceen
dc.identifier.isbn9781457702204
dc.identifier.urihttps://hdl.handle.net/10023/9856
dc.description.abstractComputer based analysis of Electronic Health Records (EHRs) has the potential to provide 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 introduces a novel algorithm that uses machine learning for the discovery of longitudinal patterns in the diagnoses of diseases. Two key technical novelties are introduced: one in the form of a novel learning paradigm which enables greater learning specificity, and another in the form of a risk driven identification of confounding diagnoses. We present a series of experiments which demonstrate the effectiveness of the proposed techniques, and which reveal novel insights regarding the most promising future research directions.
dc.format.extent1219266
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC Internal medicineen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccRCen
dc.titleTowards sophisticated learning from EHRs : increasing prediction specificity and accuracy using clinically meaningful risk criteriaen
dc.typeConference itemen
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doi10.1109/EMBC.2016.7591226


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