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. 2019 Dec 2;9:18113. doi: 10.1038/s41598-019-54653-6

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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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Comparing the prediction accuracy of the linear and non-linear VAE and PW methods in all patients from the test dataset. Boxplots present summaries of prediction using MAE (dB) over the varying number of latent dimensions. Each column indicates the number of visits used in prediction (3: left, 5: middle, 7:right), and the rows indicate how many visits into the future are predicted (1–5). The PW prediction is given in blue, and any model that is significantly smaller than PW is given in red. Significance is based on a one-sided Wilcoxon signed rank tests, using a Bonferroni corrected type-1 error, 0.05/900 ≈ 0.00006. All predictions are averaged across cross-validation folds.