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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.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.otherPURE: 247586510
dc.identifier.otherPURE UUID: 7c1f2fd0-0830-437d-919b-dd1751eb7beb
dc.identifier.otherScopus: 85009126420
dc.identifier.otherWOS: 000399823502199
dc.identifier.urihttp://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.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)en
dc.rights© 2016, IEEE. This work has been 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. The final published version of this work is available at ieeexplore.ieee.org / https://doi.org/10.1109/EMBC.2016.7591226en
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.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1109/EMBC.2016.7591226


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