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. 2019 Nov 27;9:17672. doi: 10.1038/s41598-019-53889-6

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© The Author(s) 2019

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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Example showing the ICP-MAP-CPP-GCS algorithm for a patient with a fatal 30-day outcome. The upper part shows the prediction (red line, higher value for higher probability of death) and the trends of intracranial pressure (ICP, blue line), mean arterial pressure (MAP, orange line), cerebral perfusion pressure (CPP, green line), eye response (ER, black dots) and motor response (MR, purple crosses). The lower part shows feature importance with time (red color indicating that a high feature value increases probability of death and blue color indicating that a high value increase probability of survival). The patient’s predicted risk of 30-day mortality according to the IMPACT-TBI based model was 42%.