Show simple item record

Files in this item

Thumbnail

Item metadata

dc.contributor.authorLi, Jieyi
dc.contributor.authorArandelovic, Ognjen
dc.date.accessioned2017-08-16T11:30:32Z
dc.date.available2017-08-16T11:30:32Z
dc.date.issued2017-07-11
dc.identifier.citationLi , J & Arandelovic , O 2017 , Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk . in 2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC) . , 8037782 , IEEE , pp. 4199-4202 , 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 , Jeju Island , Korea, Democratic People's Republic of , 11/07/17 . https://doi.org/10.1109/EMBC.2017.8037782en
dc.identifier.citationconferenceen
dc.identifier.otherPURE: 250828407
dc.identifier.otherPURE UUID: 93dcdd4b-c6d0-44a4-a07c-4c1379739c24
dc.identifier.otherScopus: 85032186573
dc.identifier.otherWOS: 000427085304158
dc.identifier.urihttp://hdl.handle.net/10023/11489
dc.description.abstractComputer science and machine learning in particular are increasingly lauded for their potential to aid medical practice. However, the highly technical nature of the state of the art techniques can be a major obstacle in their usability by health care professionals and thus, their adoption and actual practical benefit. In this paper we describe a software tool which focuses on the visualization of predictions made by a recently developed method which leverages data in the form of large scale electronic records for making diagnostic predictions. Guided by risk predictions, our tool allows the user to explore interactively different diagnostic trajectories,or display cumulative long term prognostics, in an intuitive and easily interpretable manner.
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartof2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)en
dc.rights© 2017, 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 https://doi.org/10.1109/EMBC.2017.8037782en
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectQH301 Biologyen
dc.subjectRC Internal medicineen
dc.subjectNDASen
dc.subject.lccQA75en
dc.subject.lccQH301en
dc.subject.lccRCen
dc.titleIntuitive and interpretable visual communication of a complex statistical model of disease progression and risken
dc.typeConference itemen
dc.description.versionPostprinten
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1109/EMBC.2017.8037782


This item appears in the following Collection(s)

Show simple item record