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. 2018 Feb 23;6(1):e11. doi: 10.2196/medinform.8960

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©Brett K Beaulieu-Jones, Daniel R Lavage, John W Snyder, Jason H Moore, Sarah A Pendergrass, Christopher R Bauer. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.02.2018.

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

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Imputation root mean square error (RMSE) for a subset of 10,000 patients from simulation 4. A total of 12 imputation methods were tested (x-axis), and each color corresponds to a Logical Observation Identifiers Names and Codes (LOINC) code. The black line shows the theoretical error from random sampling. FI: fancyimpute; KNN: k-nearest neighbors; MICE: Multivariate Imputation by Chained Equations; pmm: predictive mean matching; RF: random forest; SVD: singular value decomposition.