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. 2022 Aug 25;12:14489. doi: 10.1038/s41598-022-18793-6

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© The Author(s) 2022

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 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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Schematic representation of EHR processing. Starting from the EHR subset with sufficient information for classification (‘Included EHR’) EHR are classified into 3 CKD status groups based on clinical diagnosis, risk and other pointers for CKD in the record. Subsequently records with sufficient visits and blood/urine data prior to the evaluation visit are selected. Finally the EHR are split randomly in training and test sets. For the ‘CKD probable’ status group the training set is not shown as it was not used in the study. EHR, electronic health record.