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. 2022 Dec 8;12:21247. doi: 10.1038/s41598-022-25472-z

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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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Evaluation of the TCN model with all rebalancing methods for hold-out validation cohort. Binary classification tasks (4) ordered by rows; evaluation metrics (4) ordered by columns. Dashed lines (red/black) represent TCN and best-in-task (all models) performance without rebalancing. For the three tasks where possible (LOS > 7, ICU mortality, hospital mortality), methods are evaluated for rebalancing ratios of 1:1, 1:2, 1:3, 1:4, and 1:5, otherwise only 1:1. TCN, temporal convolutional network; AUROC, area under the receiver-operating characteristics; AUPRC, area under the precision-recall curve; LOS, length of stay; BL, borderline; SVM, support vector machine; SMOTE, synthetic minority oversampling technique.