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. 2019 Aug 14;9:11862. doi: 10.1038/s41598-019-48263-5

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© The Author(s) 2019

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Time-series data pattern extraction with deep learning. Artificial intelligence extracted raw features during the 6 months prior to the reference point of prediction for selected 24 factors to reveal time series patterns relating to 6-month DKD aggravation, using a convolutional autoencoder (CAE) and inverse analysis. Red and blue mean high and low values, respectively, and brightness of the color mean the magnitude of the values.