Intuitive and interpretable visual communication of a complex statistical model of disease progression and risk
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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.
Li , 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 . DOI: 10.1109/EMBC.2017.8037782conference
2017 IEEE 39th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC)
© 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.2017.8037782
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