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dc.contributor.authorAndrei, Victor
dc.contributor.authorArandjelovic, Ognjen
dc.date.accessioned2016-11-21T09:30:14Z
dc.date.available2016-11-21T09:30:14Z
dc.date.issued2016-08-16
dc.identifier.citationAndrei , V & Arandjelovic , O 2016 , Identification of promising research directions using machine learning aided medical literature analysis . in 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) . , 7591231 , IEEE , pp. 2471-2474 , 38th International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016 , Orlando , United States , 16/08/16 . DOI: 10.1109/EMBC.2016.7591231en
dc.identifier.citationconferenceen
dc.identifier.isbn9781457702204
dc.identifier.otherPURE: 247649826
dc.identifier.otherPURE UUID: 8a4104a1-d099-41ee-b6ec-49339755bc0c
dc.identifier.otherArXiv: http://arxiv.org/abs/1607.04660v1
dc.identifier.otherScopus: 85009080390
dc.identifier.urihttp://hdl.handle.net/10023/9853
dc.description.abstractThe rapidly expanding corpus of medical research literature presents major challenges in the understanding of previous work, the extraction of maximum information from collected data, and the identification of promising research directions. We present a case for the use of advanced machine learning techniques as an aide in this task and introduce a novel methodology that is shown to be capable of extracting meaningful information from large longitudinal corpora, and of tracking complex temporal changes within it.en
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.2016.7591231en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectRC Internal medicineen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccRCen
dc.titleIdentification of promising research directions using machine learning aided medical literature analysisen
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews. School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1109/EMBC.2016.7591231


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