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. 2019 Dec 17;19(Suppl 8):259. doi: 10.1186/s12911-019-0985-7

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© The Author(s) 2019

Open Access 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. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 3 — The architecture of our proposed RNNDAE model. Multi-hot vectors (x_t) with time series are added by a Gaussian noise and then encoded by a GRU_encoder model into the patient vector (c). Given the patient vector, another GRU_decoder model is used to decode in order to make the input (x_t) and the output (y_t) are consistent as much as possible