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dc.contributor.authorOsuala, Richard
dc.contributor.authorLi, Jieyi
dc.contributor.authorArandelovic, Ognjen
dc.date.accessioned2019-04-08T16:30:07Z
dc.date.available2019-04-08T16:30:07Z
dc.date.issued2019-04
dc.identifier.citationOsuala , R , Li , J & Arandelovic , O 2019 , ' Bringing modern machine learning into clinical practice through the use of intuitive visualization and human-computer interaction ' , Augmented Human Research , vol. 4 , 3 . https://doi.org/10.1007/s41133-019-0012-7en
dc.identifier.issn2365-4317
dc.identifier.otherPURE: 257572909
dc.identifier.otherPURE UUID: 4c4d37e0-3c03-4d2e-875e-7cacce0c585a
dc.identifier.urihttp://hdl.handle.net/10023/17484
dc.description.abstractThe increasing trend of systematic collection of medical data (diagnoses, hospital admission emergencies, blood test results, scans, etc) by healthcare providers offers an unprecedented opportunity for the application of modern data mining, pattern recognition, and machine learning algorithms. The ultimate aim is invariably that of improving outcomes, be it directly or indirectly. Notwithstanding the successes of recent research efforts in this realm, a major obstacle of making the developed models usable by medical professionals (rather than computer scientists or statisticians) remains largely unaddressed. Yet, a mounting amount of evidence shows that the ability to understand and easily use novel technologies is a major factor governing how widely adopted by the target users (doctors, nurses, and patients, amongst others) they are likely to be. In this work we address this technical gap. In particular, we describe a portable, web-based interface that allows healthcare professionals to interact with recently developed machine learning and data driven prognostic algorithms. Our application interfaces a statistical disease progression model and displays its predictions in an intuitive and readily understandable manner. Different types of geometric primitives and their visual properties (such as size or colour) are used to represent abstract quantities such as probability density functions, the rate of change of relative probabilities, and a series of other relevant statistics which the heathcare professional can use to explore patients’ risk factors or provide personalized, evidence and data driven incentivization to the patient.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofAugmented Human Researchen
dc.rightsCopyright The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectHealth careen
dc.subjectDataen
dc.subjectVisualizationen
dc.subjectMedicineen
dc.subjectPatienten
dc.subjectInteractionen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subjectR Medicine (General)en
dc.subjectT Technologyen
dc.subjectT-NDASen
dc.subject.lccQA75en
dc.subject.lccR1en
dc.subject.lccTen
dc.titleBringing modern machine learning into clinical practice through the use of intuitive visualization and human-computer interactionen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.School of Computer Scienceen
dc.identifier.doihttps://doi.org/10.1007/s41133-019-0012-7
dc.description.statusPeer revieweden


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