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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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Representative examples of the distribution of blood pH and blood urea nitrogen (BUN) illustrating the effect of discretisation by ‘binning’. In our method, continuous variables are discretised by distribution frequency so that all data types can be handled in the same way in the model. Colours exemplify 5 or 20 discrete categories established by our pipeline for any continuous variable, demonstrating outlier category detection and the increased granularity in populous intervals found by percentile-based quantization. For BUN the distribution is fairly continuous and binning creates a representation which naturally encodes the concepts of ‘high’ or ‘low’ within the distribution. For variables such as pH however, the discretisation also places artefactual values into one (a) or more (b) ‘outlier’ bins. If artefacts are random, the model should be able to learn that such data points have no predictive value and can therefore be ignored.