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. 2021 Mar 19;4:53. doi: 10.1038/s41746-021-00414-7

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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 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. 1 — For each sub-challenge, data were split into training and test portions. Participants were provided with the mobile sensor data for both the training and test portions, along with the demographic (SC1 only) and meta-data, and diagnosis or severity labels for the training portion of the data only. Participants were asked to derive features from the mobile sensor data for both the training and test portions of the data. These features were then used to train a classifier, using a standard suite of algorithms, to predict disease status or symptom severity, and predict labels in the testing portion of the data. Submissions were scored based on the accuracy of the resulting predictions.