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. 2023 May 8;29(5):1113–1122. doi: 10.1038/s41591-023-02332-5

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© The Author(s) 2023

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — a, Distributions for age at pancreatic cancer diagnosis in the two cohorts. b,c, The Danish (DK) dataset has a longer median length of disease trajectories but lower median number of disease codes per patient compared to the US-VA dataset, so the ML process, independently in each dataset, has to cope with very different distributions of disease trajectories in terms of length of trajectories and density of the number of disease codes. Color level indicates the number of patients in a given bin. d,e, Background check on the distribution disease codes in the clinical records: prevalence of known risk factors in cancer versus non-cancer patients in the DK (d) and US-VA (e) datasets, counting whether a disease code occurred at least once in a patient’s history previous to their pancreatic cancer code (cancer) or 2 years previous to the end of data (no cancer).