Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Computer 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.
Original languageEnglish
Title of host publication2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
PublisherIEEE
Pages4199-4202
DOIs
Publication statusPublished - 11 Jul 2017
Event39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - International Conference Centre (ICC), Jeju Island, Korea, Democratic People's Republic of
Duration: 11 Jul 201715 Jul 2017
Conference number: 38
https://embc.embs.org/2017/

Conference

Conference39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Abbreviated titleEMBC
Country/TerritoryKorea, Democratic People's Republic of
CityJeju Island
Period11/07/1715/07/17
Internet address

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