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. 2021 Jun 24;11:13205. doi: 10.1038/s41598-021-92475-7

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© The Author(s) 2021

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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Impact of clinical features on the prediction of mortality in COVID-19 patients (SHAP values). Left: Shown is the impact of feature values on the reduced models’ output for prediction of mortality of COVID-19 patients within the next 5 days. Each point represents a patient’s feature value (color-coded from blue for a low feature value to red for high feature values). Negative impact (left to the vertical line) of a feature value represents an impact towards the prediction of survival, positive impact (right to the vertical line) represents an impact towards prediction of non-survival. For example, high thrombocytes concentration levels (red) are associated with a higher probability of survival whereas low mean arterial blood pressure (blue) is associated with a lower probability of survival. Right: Mean absolute impact of each clinical feature on the model output.