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. 2024 Oct 28;26:e67135. doi: 10.2196/67135

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©Yun Kwan Kim, Won-Doo Seo, Sun Jung Lee, Ja Hyung Koo, Gyung Chul Kim, Hee Seok Song, Minji Lee. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.10.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Cross–data set external validation AUROC performance. (A) eICU after training MIMIC-IV. (B) MIMIC-IV after training eICU-CRD. AUROC: area under the receiver operating characteristic curve; DEWS: Deep Learning–Based Early Warning Score; eICU-CRD: eICU-Collaborative Research Database; KNN: k-nearest neighbors; LGBM: light gradient boosting method; LR: logistic regression; MIMIC: Medical Information Mart for Intensive Care; MLP: multilayer perceptron; NEWS: National Early Warning Score; RETAIN: reverse time attention.