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. 2020 Jul 31;11:3852. doi: 10.1038/s41467-020-17431-x

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© The Author(s) 2020

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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Fig. 1 — Each patient’s data from the EHR is used as input in the TCN prediction module. Based on this data, the model makes a prediction, such as a 78% risk of AKI. The DTD explanation module then explains the TCN predictions in terms of input variables. P, plasma; eGFR, estimated Glomerular filtration rate; DTD, deep Taylor decomposition; TCN, temporal convolutional network; xAI-EWS, explainable artificial intelligence early warning system.