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. 2024 Jun 24;4(7):1014–1027. doi: 10.1038/s43587-024-00657-5

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© The Author(s) 2024

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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Fig. 1 — a, Study population and inclusion and exclusion criteria. b, Data division into the training, validation and testing datasets in prospective fashion. c, Features included in the model, either treated longitudinally or fixed over time (different types of features and model inputs are color-coded in c and d) with an example of longitudinal features available for an individual across three years. n denotes the number of features in different categories. d, Graphical representation of the RNN model. Longitudinal records were embedded and then, together with an age sequence, used as inputs for a recurrent layer. Fixed-over-time features were also added before the output layer.