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. 2020 Dec 17;10:22129. doi: 10.1038/s41598-020-79142-z

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© The Author(s) 2020

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Model variant comparison. The left panel demonstrates that employing the recurrent LSTM architecture after the model embedding layer leads to a significant increase in model performance. The right panel depicts the impact of differing the number of continuous value percentile bins. When considering 5, 10, and 20 bins, we find that 10 offers the best performance and use this in all other results. 95% Confidence intervals are bootstrapped from a 10-fold cross-validation using 10,000 resamples.