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. 2020 Aug 25;22(8):e20259. doi: 10.2196/20259

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©Ahmed Abdulaal, Aatish Patel, Esmita Charani, Sarah Denny, Nabeela Mughal, Luke Moore. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 25.08.2020.

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, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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Performance metric of an artificial neural network prognostic model for coronavirus disease (COVID-19) in a UK population. Binary cross-entropy loss for training and tests per epoch. Cross-entropy loss measures the performance of a model that outputs a prediction between 0 and 1. It is a measure of how far the predictions made by the model are from the truth. As loss decreases, the probabilities estimated by the model match the actual target value (in this case, correct mortality predictions) more closely.